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In this paper, we study the following pattern search problem: Given a pair of point sets A and B in fixed dimensional space R^d, with |B| = n, |A| = m and n >= m, the pattern search problem is to find the translations T's of A such that each of the identified translations induces a matching between T(A) and a subset B' of B with cost no more than some given threshold, where the cost is defined as the minimum bipartite matching cost of T(A) and B'. We present a novel algorithm to produce a small set of candidate translations for the pattern search problem. For any B' subseteq B with |B'| = |A|, there exists at least one translation T in the candidate set such that the minimum bipartite matching cost between T(A) and B' is no larger than (1+epsilon) times the minimum bipartite matching cost between A and B' under any translation (i.e., the optimal translational matching cost). We also show that there exists an alternative solution to this problem, which constructs a candidate set of size O(n log^2 n) in O(n log^2 n) time with high probability of success. As a by-product of our construction, we obtain a weak epsilon-net for hypercube ranges, which significantly improves the construction time and the size of the candidate set. Our technique can be applied to a number of applications, including the translational pattern matching problem.

For those patients with hepatocellular carcinoma (HCC), it is really a heavy burden. Herein, the immune genes of HCC were analyzed in groups to determine prognostic biomarkers related to immune genes in HCC. The mRNA data, clinical data in TCGA-LIHC dataset, and immune gene in the ImmPort database were collected for the combining usage with K -means concordance clustering to cluster HCC patients according to the immune gene matrix. Based on ssGSEA analysis result, HCC patients were sorted into high- and low-immune subtypes, and survival curve presented that patients in high-immune subtypes had a better prognosis. Subsequently, differential expression analysis was performed to obtain immune-related differentially expressed genes (IRGs). Cox and lasso analyses were performed for obtaining five optimal immune genes related to prognosis, and a risk assessment model was then established. Patient samples in the training and validation sets were, respectively, divided into high- and low-risk groups. K - M survival curves presented a better prognosis of patients in the low-risk group than in the high-risk group. The ROC curve indicated that this model was finely used for the prediction of prognosis. In addition, immune infiltration assessment revealed that NR0B1 and FGF9 had potential to impact the tumor immune microenvironment. Finally, using qRT-PCR and transwell assays, it was demonstrated that the macrophage chemotaxis was enhanced when NR0B1 and FGF9 were highly expressed in HCC cells. In general, a 5-gene prognostic risk assessment model was constructed based on immune genes and bioinformatics analysis methods, which provides some reference for the prognosis of HCC as well as immunotherapy.

Using natural-language feedback to guide image generation and manipulation can greatly lower the required efforts and skills. This topic has received increased attention in recent years through refinement of Generative Adversarial Networks (GANs); however, most existing works are limited to single-round interaction, which is not reflective of real world interactive image editing workflows. Furthermore, previous works dealing with multi-round scenarios are limited to predefined feedback sequences, which is also impractical. In this paper, we propose a novel framework for Text-based Interactive image generation and manipulation (TiGAN) that responds to users' natural-language feedback. TiGAN utilizes the powerful pre-trained CLIP model to understand users' natural-language feedback and exploits contrastive learning for a better text-to-image mapping. To maintain the image consistency during interactions, TiGAN generates intermediate feature vectors aligned with the feedback and selectively feeds these vectors to our proposed generative model. Empirical results on several datasets show that TiGAN improves both interaction efficiency and image quality while better avoids undesirable image manipulation during interactions.

Background Some clinicians used levothyroxine (LT4) treatment for mild subclinical hypothyroidism (SCH) pregnant women (2.5 −), although the recent clinical guideline did not recommend it. It is unknown whether LT4 treatment for pregnant women with mild SCH and TPOAb− have impact on fetal growth. Therefore, the aim of the study was to investigate the effect of LT4 treatment on fetal growth and birth weight among mild SCH pregnant women with TPOAb−. Methods This was a birth cohort study including 14,609 pregnant women between 2016 and 2019 in Tongzhou Maternal and Child Health Hospital of Beijing, China. Pregnant women were divided into 3 groups as follows: Euthyroid (n = 14,285, 0.03 ≤ TSH ≤ 2.5mIU/L, normal FT4), TPOAb−; Untreated mild SCH with TPOAb− (n = 248, 2.5 − (n = 76, 2.5 Results There was no difference in fetal growth indicators and birth weight between the untreated mild SCH women with TPOAb− and the euthyroid pregnant women. But the HC Z-score was lower in the LT4 treated mild SCH women with TPOAb−, compared with the euthyroid pregnant women (β = -0.223, 95%CI: -0.422, -0.023). The LT4 treated mild SCH women with TPOAb− had lower fetal HC Z-score (β = -0.236, 95%CI: -0.457, -0.015), compared with the untreated mild SCH women with TPOAb−. Conclusions We observed that LT4 treatment for mild SCH with TPOAb− was associated with decreased fetal HC, which was not observed for untreated mild SCH women with TPOAb−. The adverse effect of LT4 treatment for mild SCH with TPOAb− provided new evidence for the recent clinical guideline.

We consider the problem of clustering a set of uncertain data, where each data consists of a point-set indicating its possible locations. The objective is to identify the representative for each uncertain data and group them into k clusters so as to minimize the total clustering cost. Different from other models, our model does not assume that there is a probability distribution for each uncertain data. Thus, all possible locations need to be considered to determine the representative. Existing methods for this problem are either impractical or have difficulty to handle large-scale datasets due to their pairwise-distance based global search strategy and expensive optimization computation. In this paper, we propose a novel sparse Non-negative Matrix Factorization (NMF) method which measures the similarity of uncertain data by their most commonly shared features. A divide-and-conquer approach is adopted to remarkably improve the efficiency. A novel diagonal l0-constraint and its l1 relaxation are proposed to overcome the challenge of determining the representatives. We give a detailed analysis to show the correctness of our method, and provide an effective initialization and peeling strategy to enhance the ability of processing large-scale datasets. Experimental results on some benchmark datasets confirm the effectiveness of our method.

Brain tumors are the deadliest and most difficult to treat of all forms of cancer. Preoperative classification of brain tumors is conducive to the development of corresponding treatment plan. Take pituitary tumors as an example. Precisely judging the image data of pituitary tumor texture before surgery can provide a basis for the selection of surgical plan and prognosis. However, the existing methods require manual intervention, and the efficiency and accuracy are not high. In this paper, we proposed an automatic brain tumor texture diagnosis method for uneven sequence image data. First, for the small sample of pituitary tumor MRI image data, the T1 and T2 sequence data are uneven or missing; we used the CycleGAN model to perform data conversion between different domains to obtain a completely sampled MRI spatial sequence. Then, we used texture analysis+pseudo-label learning to label pituitary tumor data of some unknown labels. After that, we used the improved U-Net model based on CBAM to optimize feature extraction for pituitary tumor image data. Finally, we used the CRNN model to classify the degree of pituitary tumor texture based on the advantages of sequence data. The entire process only needs to provide labels for the entire sequence data, and the efficiency is greatly improved, with an accuracy rate of 94.23%.

Bo Xie,1,* Yi Chen,2,* Yebei Hu,2 Yan Zhao,2 Haixin Luo,2 Jinhui Xu,1 Xiuzu Song1 1Department of Dermatology, Hangzhou Third People’s Hospital, Affiliated Hangzhou Dermatology Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, People’s Republic of China; 2Department of Dermatology, Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310009, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiuzu Song, Department of Dermatology, Hangzhou Third People’s Hospital, Affiliated Hangzhou Dermatology Hospital, Zhejiang University School of Medicine, West Lake Road 38, Hangzhou, 310009, People’s Republic of China, Tel +86-571-87823102, Email songxiuzu@sina.comObjective: The treatment of vitiligo is often challenging to dermatologists. There is ample evidence to suggest that hydroxychloroquine (HCQ) is effective for vitiligo treatment; nonetheless, the underlying mechanism remains unknown. In the present study, we sought to uncover the molecular targets of HCQ by an integrated network-based pharmacologic and transcriptomic approach.Methods: The potential targets of HCQ were retrieved from databases based on the crystal structure. Targets related to vitiligo were screened and intersected with potential targets of HCQ. A protein-protein interaction network of the intersected targets was generated. Interactions between the targets were verified by molecular docking. Moreover, human vitiligo immortalized melanocytes (PIG3V) were evaluated after treatment with HCQ (1μg/mL) for 24h. The total RNA of PIG3V was extracted and determined by RNA-seq transcriptomics for differential gene expression analysis. Network pharmacology was then used to identify the relationships between putative targets of HCQ and differentially expressed genes.Results: Molecular docking analysis revealed four putative key targets (ACHE, PNMT, MC1R, and VDR) of HCQ played important roles in vitiligo treatment. According to the transcriptomic results, the melanosomal biogenesis-related gene BLOC1S5 was upregulated 138005.020 fold after HCQ treatment. Genes related to protein repair (MSRB3) and anti-ultraviolet (UV) effect (UVSSA) were upregulated 4.253 and 2.603 fold, respectively, after HCQ treatment.Conclusion: The expression of the BLOC1S5 gene is significantly upregulated, indicating upregulated melanosomal biogenesis after HCQ treatment. In addition, HCQ yields a protective effect on melanocytes by upregulating genes associated with damaged protein repair (MSRB3) and anti-UV effect (UVSSA). The protective effects of HCQ are mediated by binding to putative targets ACHE, PNMT, MC1R, and VDR according to network pharmacology and docking verification.Keywords: vitiligo, hydroxychloroquine, treatment, pigmentation, melanocyte protection

IntroductionThe aim of the study was to develop a simple tool for early identification of high-risk patients with recurrent acute pancreatitis (RAP) progression to chronic pancreatitis (CP) in primary hospitals or outpatient clinics.Material and methodsThis retrospective cohort study included 265 patients with RAP.ResultsA nomogram for RAP progression to CP was developed and the C-index of the model was 0.817 (95% CI: 0.72–0.91). Patients were divided into two risk groups according to the nomogram prediction scores and a higher proportion of patients in the high-risk group progressed to CP.ConclusionsThe nomogram provided a means of predicting which patients were at high risk of progression to CP.

Based on the diversity and importance of phycospheric microorganisms as well as their complex interaction with algae, their ecological correlation with algal bloom events has become a research topic of great interest that remains unclear in the natural dynamic process of harmful macroalgal blooms (HMBs). The world’s largest green tides caused by macroalgae Ulva prolifera have occurred in the Yellow Sea for 16 consecutive years, and seriously affected the coastal ecosystem. Here, we monitored the spatiotemporal dynamics of the phycospheric bacterial community during the U. prolifera green tide bloom. The 73-day continuous field survey covered the whole process of initial invasion until the extinction of the green tide. The phycospheric bacterial community has a higher richness than the control seawater bacteria; in addition, it has more enriched taxa with organic nutrition preference and environmental pressure adaption, such as Bacteroidetes, Firmicutes, and Desulfobacterota. The same differences in populations and functional profiles were observed among the epiphytic bacterial and phycospheric seawater bacterial communities, which may be driven by the spatial heterogeneity of biogenic elements (e.g., organic nutrients and oxygen) among the habitats. Significant succession occurred in both the epiphytic bacterial and phycospheric seawater bacterial communities; their diversity and richness exhibited significant heterogeneity variation patterns, and their community structure became more similar in late phases. Our study revealed that macroalgal phycospheric microbial communities have a highly complex and dynamic composition and variable ecological functions, which indicate that phycospheric microorganisms are closely related to the fate of HMBs and may have the profound effect coastal biogeochemical cycles.

Optimal Vitamin D (VitD) status and thyroid function are essential for pregnant women. This study aimed to explore associations between dynamic VitD status and thyroid function parameters in each trimester and throughout the pregnancy period. Information on all 8828 eligible participants was extracted from the Peking University Retrospective Birth Cohort in Tongzhou. Dynamic VitD status was represented as a combination of deficiency/sufficiency in the first and second trimesters. Thyroid function was assessed in three trimesters. The associations between VitD and thyroid function were assessed by multiple linear regression and generalized estimating equation models in each trimester and throughout the pregnancy period, respectively. The results indicated that both free thyroxine (fT4; β = 0.004; 95%CI: 0.003, 0.006; p < 0.001) and free triiodothyronine (fT3; β = 0.009; 95%CI: 0.004, 0.015; p = 0.001) had positive associations with VitD status in the first trimester. A VitD status that was sufficient in the first trimester and deficient in the second trimester had a lower TSH (β = −0.370; 95%CI: −0.710, −0.031; p = 0.033) compared with the group with sufficient VitD for both first and second trimesters. In conclusion, the associations between VitD and thyroid parameters existed throughout the pregnancy. Maintaining an adequate concentration of VitD is critical to support optimal thyroid function during pregnancy.

k-Means is a well-studied clustering problem that finds applications in many fields related to unsupervised learning. It is known that k-means clustering is highly sensitive to the isolated points (called outliers). Such outliers can significantly influence the final cluster configuration and should be removed to obtain quality solutions. In this paper, we study the k-means with outliers problem. Given a set of data points, the problem is to remove a set of outliers such that the k-means clustering cost of the remaining points is minimized. Designing efficient algorithms for this problem remains an active area of research due to its important role in dealing with noisy data. We consider a relaxed objective function and propose a local search algorithm for k-means with outliers. It is shown that the algorithm has better performance guarantees than previously implemented methods. In particular, it yields a constant-factor bi-criteria approximation solution to the problem. Moreover, we show experimentally that the algorithm performs much better than its provable guarantee and dominates other state-of-the-art methods for the problem.

SummaryIn this study, differential mRNA expression patterns of prolactin receptor (PRLR) in the hypothalamus and gonads, and the correlation with follicle stimulating hormone (FSH) and luteinizing hormone (LH) in striped hamster serum from spring, summer, autumn and winter were analyzed. Mature female and male striped hamsters in oestrus were used. Expression levels of PRLR in the hypothalamus, ovaries and testis from the summer and winter individuals were significantly higher compared with levels from the spring and autumn, whereas FSH and LH serum concentrations from summer and winter individuals were significantly lower compared with that from the spring and autumn. PRLR expression levels in hypothalamus, ovaries and testis were negatively correlated with FSH and LH serum concentrations, illustrating that PRLR might negatively regulate seasonal reproductive activity. PRLR expression levels in ovaries and testes were significantly higher compared with levels in the hypothalamus, suggesting that the regulative effects of PRLR in gonads might be significantly higher compared with that in the hypothalamus. Furthermore, PRLR expression levels from the spring, summer, autumn and winter seasons in the hypothalamus and gonads were significantly higher in females compared with levels in males, indicating that the regulative effect of PRLR might be sex dependent. Taken together, this study helps to understand in depth the seasonal regulative reproduction mechanism of striped hamsters to reasonably control population abundance.

Background A prospective interventional study comparing outcomes in critically ill patients receiving intermittent infusion (II) or continuous infusion (CI) of vancomycin during continuous venovenous hemofiltration (CVVH) is lacking. The objective of this study was to compare the pharmacokinetic/pharmacodynamics (PK/PD) target attainment, therapeutic efficacy and safety among critically ill patients who received CI or II of vancomycin in a prospective interventional trial and to explore the correlations of effluent flow rate (EFR) with PK/PD indices. Methods This prospective interventional study was conducted in two independent intensive care units (ICUs) from February 2021 to January 2022. Patients in one ICU were assigned to receive CI (intervention group) of vancomycin, whereas patients in the other ICU were assigned to receive II regimen (control group). The primary outcome was to compare the PK/PD target attainment, including target concentration and target area under the curve over 24 h to minimum inhibitory concentration (AUC24/MIC). Results Overall target attainment of PK/PD indices was higher with CI compared with II, irrespective of target concentration (78.7% vs. 40.5%; P 24/MIC (53.2% vs. 28.6%; P 24/MIC (r = − 0.3811, P = 0.013) in the II group, whereas the correlation between EFR and observed concentration (r = − 0.5711, P 24/MIC (r = − 0.5458, P Conclusion As compared to II, CI of vancomycin in critically ill patients undergoing CVVH was associated with improved attainment of PK/PD indices. Furthermore, the inverse correlation of PK/PD indices with EFR was stronger among patients treated with CI of vancomycin. Trial registration The trial was registered in the Chinese clinical trial registration center (21/01/2021-No. ChiCTR2100042393).

The k-means problem is an extensively studied unsupervised learning problem with various applications in decision making and data mining. In this paper, we propose a fast and practical local search algorithm for the k-means problem. Our method reduces the search space of swap pairs from O(nk) to O(k^2), and applies random mutations to find potentially better solutions when local search falls into poor local optimum. With the assumption of data distribution that each optimal cluster has "average" size of \Omega(n/k), which is common in many datasets and k-means benchmarks, we prove that our proposed algorithm gives a (100+\epsilon)-approximate solution in expectation. Empirical experiments show that our algorithm achieves better performance compared to existing state-of-the-art local search methods on k-means benchmarks and large datasets.

The skin barrier of melasma is involved in the pathogenesis of melasma. Previous studies have shown that there are differences in the expression of epidermal lipid genes in melasma, but little is known about the epidermis lipid composition of melasma. Compared with the non-lesional skin, the content of total lipids, phosphatidic acid, phosphatidylserine, and ceramide (Cer) increased significantly in the lesional skin. Multivariate data analysis indicated that 40 individual Cer lipid species were responsible for the discrimination. In terms of acyl chain length in Cer, the expressions of very long chain (VLC) (C20-C26) and ultra-long chain (ULC) (>C26) increased significantly in the lesional skin. However, Cer[AH] had negative correlations with the activation of melanocytes in the lesional skin. Some lipid species had lower expression in lesional skin with high activation of melanocytes, as well as the high darkness. The epidermal thickness of lesional skin was higher compared with the non-lesional skin. These results suggest that Cer increased significantly in the lesional skin of melasma, possibly as a compensatory mechanism to maintain skin barrier function. Between different groups of darkness and activation of melanocytes, the change of ceramides might have correlation with the pigmentation progress of melasma.

Nowadays, crowdsourcing gains an increasing popularity as it can be adopted to solve many challenging question answering tasks that are easy for humans but difficult for computers. Due to the variety in the quality of users, it is important to infer not only the underlying ground truth of these tasks but also the users ability from the answers given by users. This problem is called Ground Truth Inference and has been studied for many years. However, since the answers collected from the users may contain sensitive information, ground truth inference raises serious privacy concern. Due to this reason, the problem of ground truth inference under local differential privacy (LDP) model has been recently studied. However, this problem is still not well understood and even some basic questions have not been solved yet. First, it is still unknown what is the average error of the private estimators to the underlying ground truth. Secondly, we do not know whether we can infer the ability of each user under LDP model and what is the estimation error w.r.t. the underlying users ability. Finally, previous work only shows that their methods have better performance than the private major voting algorithm through experiments. However, there is still no theoretically result which shows this priority formally or mathematically. In this paper, we partially solve these problems by studying the ground truth inference problem under local attribute differential privacy (LADP) model, which is a relaxation of LDP model, and propose a new algorithm called private Dawid-Skene method, which is motivated by the classical Dawid-Skene method. Specifically, we first provide the estimation errors for both ability of users and the ground truth under some assumptions of the problem if the algorithm start with some appropriate initial vector. Moreover, we propose an explicit instance and show that the estimation error of the ground truth achieved by the private major voting algorithm is always greater than the error achieved by our method.

In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions. We propose a new method, called DP-Thresholding, to achieve a non-trivial l 2 -norm based error bound whose dependence on the dimension drops to logarithmic instead of polynomial, it is significantly better than the existing ones, which add noise directly to the empirical covariance matrix. We also extend the l 2 -norm based error bound to a general l w -norm based one for any 1 ≤ w ≤ ∞ , and show that they share the same upper bound asymptotically. Our approach can be easily extended to local differential privacy. Experiments on the synthetic datasets show results that are consistent with theoretical claims.

The social behavior mechanisms have not been thoroughly reported in the solitary female striped dwarf hamster (Cricetulus barabensis). In this study, the handling bag test and neutral arena measurements were used to detect the changes of aggression in the face of rivals of different genders of wild striped dwarf hamsters. We found that female hamsters had the highest aggressive performance in proestrus, followed by estrus, and the lowest in metestrus and the dioestrus, and the increased aggression during the proestrus or estrus period was low-intensity aggression such as intimidation, shock, boxing and counterattack, or even ritualized non-harmful behaviors to drive away opponents. When confronted with male individuals, aggression in females decreased significantly during estrus. The concentration of plasma estradiol was the highest in estrus and the lowest in metestrus and dioestrus. In contrast, estrogen receptor 2 relative expression in the hypothalamus is the lowest in proestrus and highest in metestrus and dioestrus. Besides, both estradiol levels in plasma and estrogen receptor 2 mRNA in the hypothalamus were associated with aggression. These results will broaden our understanding of the molecular mechanism of how breeding phenotype is an essential driver in changing the social behavior of female Cricetulus barabensis.

Background: Primary pelvis and spine osteosarcoma (PSOS) is a specific type of osteosarcoma that is difficult to treat and has a poor prognosis. In recent years, the research on osteosarcoma has been increasing, but there have been few studies on PSOS; in particular, there have been a lack of analyses with a large sample size. This study aimed to construct and validate a model to predict the overall survival (OS) of PSOS patients, as currently there are no tools available for assessing their prognosis. Methods: Data including demographic information, clinical characteristics, and follow-up information on patients with PSOS were collected from the Surveillance, Epidemiology, and End Results (SEER) database, as well as from the Spine Tumor Center of Changzheng Hospital. Variable selection was achieved through a backward procedure based on the Akaike Information Criterion (AIC). Prognostic factors were identified by univariate and multivariate Cox analysis. A nomogram was further constructed for the estimation of 1-, 3-, and 5-year OS. Calibration plots, the concordance index (C-index), and the receiver operating characteristic (ROC) were used to evaluate the prediction model. Results: In total, 83 PSOS patients and 90 PSOS patients were separately collected from the SEER database and Changzheng Hospital. In the SEER cohort, liver metastasis, lung metastasis, and chemotherapy were recognized as independent prognostic factors for OS (p < 0.05) and were incorporated to construct the initial nomogram. However, the initial nomogram showed poor predictive accuracy in internal and external validation. Then, we shifted our focus to the Changzheng data. Lung metastasis involving segments, Eastern Cooperative Oncology Group (ECOG) performance score, alkaline phosphatase (ALP) level, and en bloc resection were ultimately identified as independent prognostic factors for OS (p < 0.05) and were further incorporated to construct the current nomogram, of which the bias-corrected C-index was 0.834 (0.824–0.856). The areas under the ROC curves (AUCs) of the current nomogram regarding 1-, 3-, and 5-year OS probabilities were 0.93, 0.96, and 0.92, respectively. Conclusion: We have developed a predictive model with satisfactory performance and clinical practicability, enabling effective prediction of the OS of PSOS patients and aiding clinicians in decision-making.

The relationship between PPI and CPI is one of the hotspots in the study of macroeconomics. Taking China as an example, the paper tests dynamic causality between PPI and CPI during the period of 2000:M1‐2019:M12. In view of structural mutations in time series variables, the bootstrap rolling‐window causality test is applied to restudy the dynamic causal relationship. As a result, it exists bidirectional transmission mechanism between CPI and PPI, both positive transmission of PPI to CPI and reverse transmission of CPI to PPI. In addition to putting more emphasis on transmission mechanism and causality between PPI and CPI, formulation of macroeconomic policy needs to focus on the correlation between PPI and CPI, and then the government can implement more targeted measures, such as strengthening the comprehensive application of monetary policy and fiscal policy, further deepening the supply‐side reform, optimizing the industrial structure to strengthen internal relationship between CPI and PPI.

In order to enhance the CO2 dissolution rate and prolong gas–liquid contact time to improve microalgal growth, a spiral-ascending CO2 dissolver was developed to enhance CO2 mass transfer in a horiz...

This study aimed to characterize giant cell tumor of the tendon sheath (GCTTS) in spine more fully and further validate the therapeutical effect of surgcial resection for treating this neoplasm.Patients diagnosed with spinal GCTTS and received surgical resection in our hospital between January 2009 and September 2021 were identified retrospectively. The clinical data and radiological images were summarized and the clinical outcomes of patients with a follow-up period of more than 12 months were analyzed.Thirty patients with benign GCTTS and one with malignant GCTTS were included. Preoperative radiological images were available in 28 of 30 benign cases. Benign lesions were revealed as soft tissue masses centering on the facet joint with osteolytic bone destruction in 26 patients on CT, and as prevertebral or intramuscular masses without bone erosion in 2. MRI showed the signal of isointensity or hypointensity on T1 weighted images (T1WI) in 25 patients and slightly hyperintense in three. On T2 weighted images (T2WI), 17 lesions displayed homogeneous hypointense signal, and eight lesions possessed heterogeneous signals. The remaining three lesions featured slightly hyperintense signal on T2WI. Follow-up data were available in 23 of 30 benign cases treated with gross-total resection, and two patients experienced recurrence.Spinal GCTTS should be suspected in cases with features such as the mass mainly involving the posterior bone elements, the lack of intralesional calcification, T2-weighted dark signals, and free of any cancer. Gross-total resection is an effective means for treating spinal GCTTS.

Haixin Luo,1,&ast; Bo Xie,2,&ast; Jinhui Xu,2 Yuqi Zhu,1 Jiayi Sun,1 Yuqing Shen,1 Xiuzu Song2 1Department of Dermatology, Hangzhou Third Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 2Department of Dermatology, Hangzhou Third People’s Hospital; Affiliated Hangzhou Dermatology Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiuzu Song, Email songxiuzu@sina.comBackground: Vitiligo is an acquired skin depigmentation disease. It can be misdiagnosed at an early stage and tend to relapse. Serum markers are essential to monitoring the progression of vitiligo. Exosomal miRNAs act as the communication mediator between melanocytes and immune cells. Our study aimed to use serum exosomal miRNAs as a reference for evaluating vitiligo progression.Methods: The miRNAs were extracted from the serum exosomes of ten progressive vitiligo patients (before and after treatment) and ten healthy individuals. We profiled miRNAs expression by RNA sequencing and screened out potential miRNAs and plotted their receiver operating characteristic (ROC) curves to explore their sensitivity and specificity as prognostic biomarkers in vitiligo progression. We examined the correlation between miRNA expression and the lesion area. Different databases were used to predict gene targets of miRNAs, which were analyzed by gene ontology and Kyoto encyclopedia of genes and genomes (KEGG).Results: Our results showed that 141 miRNAs were differentially expressed in serum exosomes of progressive vitiligo patients, and 365 miRNAs were differentially expressed in these patients after treatment compared to healthy individuals. The expression of hsa-miR-487b-3p was significantly lower in these patients compared to healthy individuals. Still, there was no difference in its levels in patients after corticosteroid treatment compared to healthy controls. ROC curve analysis (area under curve = 0.840) indicated that hsa-miR-487b-3p could serve as a biomarker for the prognosis of vitiligo progression. Its expression positively correlated with the lesion area. A total of 41 target genes of hsa-miR-487b-3p were predicted via different databases. KEGG pathways were enriched in phenylalanine metabolism, glycan degradation, and protein export.Conclusion: Serum exosomal hsa-miR-487b-3p can be a biomarker to detect vitiligo progression. The predicted target genes of hsa-miR-487b-3p were enriched in catabolism. Thus, its in progressive vitiligo may accelerate catabolism in melanocytes and cause its impairment.Keywords: progressive vitiligo, exosome, miRNA, biomarker

Novel anode materials for lithium-ion batteries were synthesized by

Truth Discovery is an important learning problem arising in data analytics related fields. It concerns about finding the most trustworthy information from a dataset acquired from a number of unreliable sources. The problem has been extensively studied and a number of techniques have already been proposed. However, all of them are of heuristic nature and do not have any quality guarantee. In this paper, we formulate the problem as a high dimensional geometric optimization problem, called Entropy based Geometric Variance. Relying on a number of novel geometric techniques, we further discover new insights to this problem. We show, for the first time, that the truth discovery problem can be solved with guaranteed quality of solution. Particularly, it is possible to achieve a ( 1 + ϵ ) -approximation within nearly linear time under some reasonable assumptions. We expect that our algorithm will be useful for other data related applications.

In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a lower bound of Ω ( s 2 log ⁡ p n ϵ 2 ) on the ϵ non-interactive private minimax risk in the metric of squared spectral norm, where s is the row sparsity of the underlying covariance matrix, n is the sample size, and p is the dimensionality of the data. We show that the lower bound is actually tight, as it matches a previous upper bound. Our main technique for achieving this lower bound is a general framework, called General Private Assouad Lemma, which is a considerable generalization of the previous private Assouad lemma and can be used as a general method for bounding the private minimax risk of matrix-related estimation problems.

In this paper we study the problem of estimating stochastic linear combination of non-linear regressions, which has a close connection with many machine learning and statistical models such as non-linear regressions, the Single Index, Multi-index, Varying Coefficient Index Models and Two-layer Neural Networks. Specifically, we first show that with some mild assumptions, if the variate vector x is multivariate Gaussian, then there is an algorithm whose output vectors have ℓ2-norm estimation errors of O(√p/n) with high probability, where p is the dimension of x and n is the number of samples. Then we extend our result to the case where x is sub-Gaussian using the zero-bias transformation, which could be seen as a generalization of the classic Stein's lemma. We also show that with some additional assumptions there is an algorithm whose output vectors have ℓ∞-norm estimation errors of O(1/√p + √p/n) with high probability. Finally, for both Gaussian and sub-Gaussian cases we propose a faster sub-sampling based algorithm and show that when the sub-sample sizes are large enough then the estimation errors will not be sacrificed by too much. Experiments for both cases support our theoretical results. To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.

Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i.e., the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the learned models. To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and offline settings. Specifically, for the online setting, we first introduce a differentially private algorithm (called OnPairStrC) for strongly convex loss functions. Then, we extend this algorithm to general convex loss functions and give another differentially private algorithm (called OnPairC). For the offline setting, we also present two differentially private algorithms (called OffPairStrC and OffPairC) for strongly and general convex loss functions, respectively. These proposed algorithms can not only learn the model effectively from the data but also provide strong privacy protection guarantee for sensitive information in the training set. Extensive experiments on real-world datasets are conducted to evaluate the proposed algorithms and the experimental results support our theoretical analysis.

In this paper, we consider the following sum query problem: Given a point set P in $${\mathbb {R}}^d$$ , and a distance-based function f(p, q) (i.e., a function of the distance between p and q) satisfying some general properties, the goal is to develop a data structure and a query algorithm for efficiently computing a $$(1+\epsilon )$$ -approximate solution to the sum $$\sum _{p \in P} f(p,q)$$ for any query point $$q \in {\mathbb {R}}^d$$ and any small constant $$\epsilon >0$$ . Existing techniques for this problem are mainly based on some core-set techniques which often have difficulties to deal with functions with local domination property. Based on several new insights to this problem, we develop in this paper a novel technique to overcome these encountered difficulties. Our algorithm is capable of answering queries with high success probability in time no more than $${\tilde{O}}_{\epsilon ,d}(n^{0.5 + c})$$ , and the underlying data structure can be constructed in $${\tilde{O}}_{\epsilon ,d}(n^{1+c})$$ time for any $$c>0$$ , where the hidden constant has only polynomial dependence on $$1/\epsilon$$ and d. Our technique is simple and can be easily implemented for practical purpose.

The Yellow Sea green tide (YSGT) is the world’s largest transregional macroalgal blooms, and the causative species Ulva prolifera (U. prolifera) suffers from ultraviolet-b radiation (UVBR) during the floating migration process. Previous study confirmed that U. prolifera displayed a wide variety of physiological responses characterized as acclimation to UVBR, while the response mechanisms against low-dose and short-term radiation (LDSTR) are not clear. A study with photosynthetically active radiation (PAR) and UVBR was designed: normal light (NL: 72 μmol photons m−2 s−1), NL+0.3 (UVBR: 0.3 W·m−2), and NL+1.6 (UVBR: 1.6 W·m−2). The results showed that high-dose UVBR inhibited photosynthesis in thalli, especially under long-term exposure, while a variety of physiological responses were observed under LDSTR. The inhibition of photosynthesis appeared to be ameliorated by the algae under LDSTR. Further analysis showed that U. prolifera achieved balancing damage by means of non-photochemical quenching (NPQ), accumulation of phenolic compounds coupled with the ASA-GSH cycle involved in the antioxidant process and enhanced photorespiratory metabolism under LDSTR. This study provides new insights into the balancing damage mechanisms of U. prolifera under LDSTR, enabling the thalli to adapt to the light conditions during the long duration and distance involved in floating migration.

Objective: The objective of the study was to assess the impact of multifaceted clinical pharmacist-led antimicrobial stewardship (AMS) program on the rational use of antibiotics for patients who receive vascular and interventional radiology therapies.Methods: A quasi-experimental retrospective intervention design with a comparison group was applied to the practice of antibiotic use in the department of vascular and interventional radiology in a Chinese tertiary hospital. We used difference-in-differences (DID) analysis to compare outcomes before and after the AMS intervention between the intervention group and control group, to determine whether intervention would lead to changes in irrationality of antibiotic prescribing, antibiotic utilization, cost of antibiotics, and length of hospital stay.Results: The DID results showed that the intervention group was associated with a reduction in the average consumption of antibiotics (p = 0.017) and cost of antibiotics (p = 0.006) and cost per defined daily dose (DDD) (p = 0.000). There were no significant differences in the mean change of total costs and length of stay between the two groups (p > 0.05). The average inappropriate score of perioperative antimicrobial prophylaxis in the intervention group declined by 0.23, while it decreased by 0.02 in the control group [0.21 (95% CI, −0.271 to −0.143); p = 0.000]. The average inappropriate score of non-surgical antimicrobial prophylaxis in the intervention group declined by 0.14, while it increased by 0.02 in the control group [0.16 (95% CI, −0.288 to −0.035); p = 0.010]. The average inappropriate score of the therapeutic use of antibiotics in the intervention group declined by 0.07, while it decreased by 0.01 in the control group [0.06 (95% CI, −0.115 to −0.022); p = 0.003].Conclusions: This study provides evidence that implementation of AMS interventions was associated with a marked reduction of antibiotic use, cost of antibiotics, and irrationality of antibiotic prescribing in China.

The Yellow Sea green tide (YSGT) is the world's largest transregional macroalgal blooms, and the causative species

There are multiple treatment modalities for periungual warts (PWs), although most are destructive and painful, limiting their application. Radiotherapy is a non-invasive method suitable for treating PW patients with contraindications to invasive procedures. To investigate the efficacy and safety of topical Tretinoin combined with Superficial X-ray therapy (SXRT) in treating PWs. This study included patients with 65 PWs who underwent treatment and a 3-month follow-up. Twenty four PWs were subjected to SXRT alone (group A). The remaining 41 PWs were subjected to SXRT combined with the application of the Tretinoin cream from the first day (group B). The overall clinical response rate, recurrence rates, cosmetic outcomes, and adverse events were observed during the follow-up period. The complete clearance rate (75% vs. 92.7% in groups A and B, respectively) and healing times (19.9 vs. 16.0 days in groups A and B, respectively) between the two groups were significantly different (p 0.046 and 0.04), indicating the combination treatment is more effective. Notably, there was no damaging or permanent deformation on the nail, and the other adverse effects were mild and bearable. Topical Tretinoin combined with SXRT therapy is an effective strategy for treating PWs, with minor side effects. It is painless and with excellent cosmetic outcomes.

Ulva prolifera green tides, one of the greatest marine ecological disasters, originate in the southern Yellow Sea of China and obtain the highest biomass in Haizhou Bay (latitude around 35° N) during northward drift. U. prolifera shows different morphologies from southern Haizhou Bay (SH) to northern Haizhou Bay (NH). Owing to the distinct nutrient environments between SH and NH, we hypothesized that thalli in NH with poor nutrients increased the surface area to volume ratio (SA:VOL) to better absorb nutrients. Here, we tested this hypothesis by comparing the SA:VOL of thalli in SH and NH. The results showed that the thalli in NH had a lower SA:VOL than those in SH, and SA:VOL had positive relationships with temperature and nutrients, contrary to the general hypothesis. The novel results suggested that morphological differences of U. prolifera were the result of developmental state rather than environmental acclimation. Indicators of reproduction (reproductive allocation ratio) were negatively related to variation in tissue contents of C, N, P, and crude protein, whereas indicators of growth (tissue contents of C, N, P, and crude protein) showed significant positive influences on SA:VOL. The results indicated that a trade‐off relationship between reproduction and growth existed in the northward drift. All the results suggested that physiological functional traits affected morphological variation of U. prolifera in different environmental conditions during the drifting of green tides. This study presents new insights into the opportunist species nature of U. prolifera through morphological variation and associated functional consequences., This manuscript highlighted a trade‐off relationship between growth and reproduction existed in the northward drift of green tides caused by U. prolifera, and all the results suggested that physiological functional traits affected morphological variation of U. prolifera in different environmental conditions during the drifting. Principal findings of the manuscript: (1) previous studies demonstrated that large‐scale green tides, mainly the genus Ulva prolifera, have occurred in the Yellow Sea of China since 2007 and obtain the highest biomass in Haizhou Bay. U. prolifera displayed distinct morphological differences during the drift northward. In order to find the reason about morphological variation of U. prolifera, we hypothesized that the thalli of U. prolifera adapt to the environmental changes by the variation in morphology to enhance nutrient uptake in northern Haizhou Bay (NH), an area with relatively less nutrients. (2) However, by comparing the surface area to volume ratio (SA:VOL), we found that the thalli in NH had a lower SA:VOL than those in SH, and SA:VOL had positive relationships with temperature and nutrients, contrary to the general hypothesis. (3) The novel results suggested that morphological differences of U. prolifera were the result of developmental state rather than environmental acclimation. Indicators of growth (Fv/Fm, Y(II)) and relative growth rate showed positive correlations with SA:VOL, whereas indicators of reproduction (reproductive allocation ratio) exhibited an opposite result and were negatively related to variation in tissue contents of C, N, P, and crude protein.