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Mesonephric adenocarcinomas (MAs) with spindle cell components are rare malignant cervical tumours. In the present study, a retrospective analysis of these tumours was performed. Clinicopathological data were gathered from electronic surgical pathology records, and both immunohistochemistry and targeted next-generation sequencing (NGS) were performed. The present study included three postmenopausal female patients diagnosed with primary uterine cervical MA with prominent spindle cell components, aged 51-60 years. All patients underwent hysterectomy with bilateral salpingo-oophorectomy and pelvic lymph node dissection. There were no recurrences or deaths after surgery. NGS analysis identified KRAS mutations in 2 cases and a PIK3-catalytic subunit α (PIK3CA) mutation in another. Spindle cell components may indicate MAs at an advanced stage. Spindle cell components in MAs are diagnostic pitfalls, and the use of immunohistochemical panels and molecular detection cases with overlapping morphological features is recommended. While KRAS mutations are the most common types of mutations in MAs with spindle cell components, the present study demonstrates that PIK3CA mutations can also occur independently in cases without KRAS mutations. [ABSTRACT FROM AUTHOR]

We introduce a novel approach to estimation problems in settings with missing data. Our proposal - the Correlation-Assisted Missing data (CAM) estimator - works by exploiting the relationship between the observations with missing features and those without missing features in order to obtain improved prediction accuracy. In particular, our theoretical results elucidate general conditions under which the proposed CAM estimator has lower mean squared error than the widely used complete-case approach in a range of estimation problems. We showcase in detail how the CAM estimator can be applied to U-Statistics to obtain an unbiased, asymptotically Gaussian estimator that has lower variance than the complete-case U-Statistic. Further, in nonparametric density estimation and regression problems, we construct our CAM estimator using kernel functions, and show it has lower asymptotic mean squared error than the corresponding complete-case kernel estimator. We also include practical demonstrations throughout the paper using simulated data and the Terneuzen birth cohort and Brandsma datasets available from CRAN. [ABSTRACT FROM AUTHOR]

We propose a deep learning-based knockoffs inference framework, DeepLINK, that guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is applicable to a broad class of covariate distributions described by the possibly nonlinear latent factor models. It consists of two major parts: an autoencoder network for the knockoff variable construction and a multilayer perceptron network for feature selection with the FDR control. The empirical performance of DeepLINK is investigated through extensive simulation studies, where it is shown to achieve FDR control in feature selection with both high selection power and high prediction accuracy. We also apply DeepLINK to three real data applications to demonstrate its practical utility. [ABSTRACT FROM AUTHOR]

Deep learning has benefited almost every aspect of modern big data applications. Yet its statistical properties still remain largely unexplored. It is commonly believed nowadays that deep neural networks (DNNs)benefit from representation all earning. To gain some statistical insights into this, we design a simple simulation setting where we generate data from some latent subspace structure with each subspace regarded as a cluster. We empirically demonstrate that the performance of DNN is very similar to that of the two-step procedure of clustering followed by classification (unsupervised plus supervised). This motivates us to ask: Does DNN indeed mimic the two-step procedure statistically? That is, do bottom layers in DNN try to cluster first and then top layers classify within each cluster? To answer this question, we conduct a series of simulation studies, and to our surprise, none of the hidden layers in DNN conduct successful clustering. In some sense, our results provide an important complement to the common belief of representational learning, suggesting that at least in some model settings, although the performance of DNN is comparable with that of the ideal two-step procedure knowing the true latent cluster information a priori, it does not really do clustering in any of its layers. We also provide some statistical insights and heuristic arguments to support our empirical discoveries and further demonstrate the revealed phenomenon on the real data application of traffic sign recognition. [ABSTRACT FROM AUTHOR]

In our previous work, the ZnIn2S4-RGO-BiVO4 Z-scheme system has been constructed successfully, which exhibits enhanced photoquantum yields and photocatalytic activity for hydrogen production. It is generally believed that morphology characteristics of catalysts is an important factor for achieving efficient photocatalytic activity. Therefore, in this work, the effect of BiVO4 morphology on photocatalytic activity of Z-scheme system was investigated. A series of BiVO4 with the morphologies of dumbbell, globose, flake, flower-like, acicular and massive shape were synthesized successfully using hydrothermal synthesis method. Then, using these BiVO4 as oxidation side photocatalyst, a series of ZnIn2S4/RGO/BiVO4 Z-scheme photocatalysts were synthesized. Their photocatalytic activities were evaluated by the hydrogen production from water under visible-light irradiation and the physicochemical properties were explored by various analytical techniques such as SEM, XRD, BET and Raman. It was found that the morphology of BiVO4 has a significant influence on the properties of ZnIn2S4/ RGO/BiVO4, such as the self-assembly of ZnIn4S4, the graphitization extents of RGO, and the special surface area of the Zscheme photocatalyst. Among the numbers Z-scheme photocatalyst, the Z-acicular shape photocatalyst has the best activity and its hydrogen production rate reaches 563.3μmol/(g⋅h). [ABSTRACT FROM AUTHOR]

Evaluating the joint significance of covariates is of fundamental importance in a wide range of applications. To this end, p-values are frequently employed and produced by algorithms that are powered by classical large-sample asymptotic theory. It is well known that the conventional p-values in Gaussian linear model are valid even when the dimensionality is a non-vanishing fraction of the sample size, but can break down when the design matrix becomes singular in higher dimensions or when the error distribution deviates from Gaussianity. A natural question is when the conventional p-values in generalized linear models become invalid in diverging dimensions. We establish that such a breakdown can occur early in nonlinear models. Our theoretical characterizations are confirmed by simulation studies. [ABSTRACT FROM AUTHOR]

Background: The protistan pathogens Cryptosporidium and Giardia can cause significant intestinal diseases in animals and humans. Cattle, particularly calves, carrying these protists can be significant reservoirs for human infections and disease. However, little is known about the genetic make-up of Cryptosporidium and Giardia populations in cattle and other ruminants in some regions of China. Results: In the present study, PCR-based tools were used to genetically characterise these protists in faecal samples from a total of 339 pre- and post-weaned calves from four distinct locations in Hubei Province using markers in the large (LSU) or small (SSU) subunits of nuclear ribosomal RNA genes. Cryptosporidium andersoni, C. bovis, C. ryanae and Giardia duodenalis assemblage E were detected in 0.6%, 10.9%, 4.1% and 22.6% of calves, respectively. Conclusions: This study is the first to report the prevalence of Cryptosporidium and Giardia in pre- and post-weaned calves in Hubei Province, and encourages large-scale molecular studies of animals and humans, in an effort to better understand the epidemiology of these enteric pathogens in China. [ABSTRACT FROM AUTHOR]

Objective: An amelogenin-derived peptide has been shown to promote remineralization of demineralized enamel in an in vitro model of initial caries induced by pH cycling. The present study examines whether the peptide exerts similar effects within the complex oral environment in vivo. Design: Specific pathogen-free Sprague-Dawley rats (n = 36) were infected with Streptococcus mutans, given ad libitum access to Diet 2000 and drinking water supplemented with sucrose (10%, w/v), and then randomly divided into three groups treated with 25 mM peptide solution, 1 g/L NaF or deionized water. Molar teeth were swabbed twice daily with the respective solutions for 24 days. Then animals were killed, their jaws were removed and caries lesions were analyzed using the quantitative light-induced fluorescence-digital (QLF-D) technique to measure changes in mineral content. To verify QLF-D results, caries were scored for lesion depth and size using the Keyes method, and analyzed using polarized light microscopy (PLM). Results: Mineral gain was significantly higher in teeth treated with peptide or NaF than in teeth treated with water (p < 0.05), based on the QLF-D results (DF and DQ). Incidence of smooth-surface and sulcal caries based on Keyes scores was similar in rats treated with peptide or NaF, and significantly lower in these groups than in rats treated with water (p < 0.05). Lesions on teeth treated with peptide or NaF were shallower, based on PLM. No significant differences were observed between molar enamel caries treated with peptide or NaF. Conclusions: This amelogenin-derived peptide can promote remineralization in a rat caries model, indicating strong potential for clinical use. [ABSTRACT FROM AUTHOR]

In traditional Chinese medicine, shikonin and its derivatives, has been used in East Asia for several years for the prevention and treatment of several diseases, including cancer. We previously identified that β,β-dimethylacrylshikonin (DA) could inhibit hepatocellular carcinoma growth. In the present study, we investigated the inhibitory effects of DA on human colorectal cancer (CRC) cell line HCT-116 in vitro and in vivo. A viability assay showed that DA could inhibit tumor cell growth in a time- and dose-dependent manner. Flow cytometry showed that DA blocks the cell cycle at G0/G1 phase. Western blotting results demonstrated that the induction of apoptosis by DA correlated with the induction of pro-apoptotic proteins Bax, and Bid, and a decrease in the expression of anti-apoptotic proteins Bcl-2 and Bcl-xl. Furthermore, treatment of HCT-116 bearing nude mice with DA significantly retarded the growth of xenografts. Consistent with the results in vitro, the DA-mediated suppression of HCT-116 xenografts correlated with Bax and Bcl-2. Taken together, these results suggest that DA could be a novel and promising approach to the treatment of CRC. [ABSTRACT FROM AUTHOR]

Time- and state-domain methods are two common approaches to nonparametric prediction. Whereas the former uses data predominantly from recent history, the latter relies mainly on historical information. Combining these two pieces of valuable information is an interesting challenge in statistics. We surmount this problem by dynamically integrating information from both the time and state domains. The estimators from these two domains are optimally combined based on a data-driven weighting strategy, which provides a more efficient estimator of volatility. Asymptotic normality is separately established for the time domain, the state domain, and the integrated estimators. By comparing the efficiency of the estimators, we demonstrate that the proposed integrated estimator uniformly dominates the other two estimators. The proposed dynamic integration approach is also applicable to other estimation problems in time series. Extensive simulations are conducted to demonstrate that the newly proposed procedure outperforms some popular ones, such as the RiskMetrics and historical simulation approaches, among others. In addition, empirical studies convincingly endorse our integration method. [ABSTRACT FROM AUTHOR]

Fusarium blight of wheat is usually caused by Fusarium graminearum, and the pathogenic fungi will secrete effectors into the host plant tissue to affect its normal physiological process, so as to make it pathogenic. The CFEM (Common in Fungal Extracellular Membrane) protein domain is unique to fungi, but it is not found in all fungi. The CFEM protein contained in F. graminearum may be closely related to pathogenicity. In this study, 23 FgCFEM proteins were identified from the F. graminearum genome. Then, features of these proteins, such as signal peptide, subcellular localization, and transmembrane domains, etc., were analyzed and candidate effectors were screened out. Sequence alignment results revealed that each FgCFEM protein contains one CFEM domain. The amino acids of the CFEM domain are highly conserved and contain eight spaced cysteines, with the exception that FgCFEM8, 9, and 15 lack two cysteines and three cysteines were missed in FgCFEM18 and FgCFEM22. A recently identified CFEM_DR motif was detected in 11 FgCFEMs, and importantly we identified two new conserved motifs containing about 29 and 18 amino acids (CFEM_WR and CFEM_KF), respectively, in some of FgCFEM proteins. Transcriptome analysis of the genes encoding CFEM proteins indicated that all the CFEM-containing genes were expressed during wheat infection, with seven and six genes significantly up- and down-regulated, respectively, compared with in planta and in vitro. Based on the above analysis, FgCFEM11 and FgCFEM23 were predicted to be F. graminearum effectors. This study provides the basis for future functional analyses of CFEM proteins in F. graminearum. [ABSTRACT FROM AUTHOR]

Polycystic ovary syndrome (PCOS) is a common gynecologic endocrine in women of childbearing age, metabolic disorders, according to the prevalence of polycystic ovary syndrome in women of childbearing age about 7% - 18%, accounting for 20% of the patients with clinical endocrine disease of department of gynaecology - 60%, pathophysiology and clinical manifestation, patients often show follicle selection obstacle, follicle stagnation in the sinus follicle period, unable to form a dominant follicle, ovulation disorders, accounted for 30% ~ 60% of patients with infertility. The purpose of this study was to investigate the efficacy of letrozole combined with human chorionic gonadotropin in the treatment of patients with polycystic ovary syndrome (pcos) and infertility, as well as the changes of reproductive endocrine hormones and HGF levels. In this paper, a random number table was used to divide the patients into letrozole group, human chorionic gonadotropin group and human chorionic gonadotropin group, with 50 cases in each group. The results showed that the ovulation rates of letrozole group, human chorionic gonadotropin group and letrozole combined with human chorionic gonadotropin group were 72.06%, 67.69% and 76.92%, respectively. The cycle pregnancy rate was 30%, 20% and 40% respectively. After the injection of human chorionic gonadotropin, the VI and VFI levels of letrozole combined with human chorionic gonadotropin group were significantly higher than those of letrozole group and human chorionic gonadotropin group, the FI levels were significantly lower than those of letrozole group and human chorionic gonadotropin group, and the ovarian volume was significantly smaller than that of letrozole group and human chorionic gonadotropin group. It is concluded that letrozole combined with human chorionic gonadotropin has an obvious therapeutic effect on patients with polycystic ovary syndrome and infertility, and can effectively improve the clinical symptoms, signs and endocrine status of patients, and improve the pregnancy rate of patients. [ABSTRACT FROM AUTHOR]

Abstract Aim Twinfilin-1 (TWF1) has been implicated in cell motility, invasion and migration. However, its exact role in lung cancer progression is still unclear. In the present study, we explored clinical and prognostic relevance of Twinfilin-1 (TWF1) levels for non-small cell lung carcinoma (NSCLC). Main methods The Cancer Genome Atlas (TCGA) dataset was analyzed for possible association between TWF1 expressions in NSCLC tissues and patient prognosis. The meta-analysis data was validated in our clinical study through techniques of immunoblotting, expression analysis and immunohistochemistry. Key findings Lung adenocarcinoma (LUAD) as well as lung squamous cell carcinoma (LUSC) showed significantly elevated expression of TWF1 compared to normal lung tissues. Univariate Cox regression analysis showed high expression of TWF1 to be independent prognostic indicator involved in overall survival (hazard ratio: 1.636; 95% CI: 1.223–2.189) and recurrence-free survival (hazard ratio: 1.551; 95% CI: 1.158–2.077) in LUAD, but not in LUSC. Similar trend was found in our clinical study. LUAD tissues reflected TWF1 overexpression to be positively correlated with grade of tumor, size and lymph node metastasis. Enhanced TWF1 expression was identified to be an independent predictor for the disadvantageous prognosis of LUAD through simultaneously both univariate as well as multivariate Cox regression analyses (both p < 0.05). Kaplan-Meier survival graphs further corroborated that poor disease prediction in the patients with LUAD was indicated through high TWF1 expression (p = 0.028). Significance Robustness and poor prognosis in LUAD correlated with TWF1 levels thus making it a suitable therapeutic target against LUAD. [ABSTRACT FROM AUTHOR]

In this study, twenty of the most common Fusarium species were molecularly characterized and inoculated on potato dextrose agar (PDA), rice and maize medium, where thirty three targeted mycotoxins, which might be the secondary metabolites of the identified fungal species, were detected by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Statistical analysis was performed with principal component analysis (PCA) to characterize the mycotoxin profiles for the twenty fungi, suggesting that these fungi species could be discriminated and divided into three groups as follows. Group I, the fusaric acid producers, were defined into two subgroups, namely subgroup I as producers of fusaric acid and fumonisins, comprising of F. proliferatum, F. verticillioides, F. fujikuroi and F. solani, and subgroup II considered to only produce fusaric acid, including F. temperatum, F. subglutinans, F. musae, F. tricinctum, F. oxysporum, F. equiseti, F. sacchari, F. concentricum, F. andiyazi. Group II, as type A trichothecenes producers, included F. langsethiae, F. sporotrichioides, F. polyphialidicum, while Group III were found to mainly produce type B trichothecenes, comprising of F. culmorum, F. poae, F. meridionale and F. graminearum. A comprehensive picture, which presents the mycotoxin-producing patterns by the selected fungal species in various matrices, is obtained for the first time, and thus from an application point of view, provides key information to explore mycotoxigenic potentials of Fusarium species and forecast the Fusarium infestation/mycotoxins contamination. [ABSTRACT FROM AUTHOR]