The ability to quantify levels of target analytes in biological samples accurately and precisely, in biomonitoring, involves the use of highly sensitive and selective instrumentation such as tandem mass spectrometers and a thorough understanding of highly variable matrix effects. Typically, matrix effects are caused by co-eluting matrix components that alter the ionization of target analytes as well as the chromatographic response of target analytes, leading to reduced or increased sensitivity of the analysis. Thus, before the desired accuracy and precision standards of laboratory data are achieved, these effects must be characterized and controlled. Here we present our review and observations of matrix effects encountered during the validation and implementation of tandem mass spectrometry-based analytical methods. We also provide systematic, comprehensive laboratory strategies needed to control challenges posed by matrix effects in order to ensure delivery of the most accurate data for biomonitoring studies assessing exposure to environmental toxicants.

High Ki-67 before NAC was a predictor for pCR in neoadjuvant setting for breast cancer patients.

P73 antisense RNA 1T (TP73-AS1 or PDAM) is a long non-coding RNA, which can regulate apoptosis through regulation of p53 signaling-related anti-apoptotic genes. An abnormal change of TP73-AS1 expression was noticed in cancers. The effects of TP73-AS1 in breast cancer (BC) growth and the underlying mechanism remain unclear so far. In the present study, the effect of TP73-AS1 in BC cell lines and clinical tumor samples was detected so as to reveal its role and function. In the present study, TP73-AS1 was specifically upregulated in BC tissues and BC cell lines and was correlated to a poorer prognosis in patients with BC. TP73-AS1 knocking down

Nearly monodisperse PbWO4 and CaWO4 microspheres have been synthesized in large scale by a surfactant-assisted solution route, in which either sodium dodecyl benzenesulfonate (SDS) or cetyltrimethyl ammonium bromide (CTAB) is used. By controlling the solution reaction conditions, such as temperature, surfactant, and pH value, we can synthesize nearly monodisperse microspheres of tetragonal tungstate (MWO4, M = Pb, Ca) composed of subunits with different shapes. The diameters of these microspheres have been found to be sensitive to the reaction conditions and could be tuned by controlling the reaction conditions. The growth process of these nearly monodisperse microspheres has been examined. The optical properties of the tungstate monodisperse microspheres in the temperature range of 20−270 K were studied.

BackgroundThe prognostic effect of tumor infiltrating CD8+ cytotoxic lymphocytes (CTLs) in breast cancer is controversial. We analyzed the association between CD8+ CTLs and survival of untreated node-negative breast cancer patients.Material and MethodsCD8+ CTLs infiltrate was evaluated by immunostaining in a cohort of 332 node-negative breast cancer patients with a median follow-up of 152 months. The prognostic significance of CD8+ CTLs for disease-free survival (DFS) and breast cancer-specific overall survival (OS) was evaluated with Kaplan-Meier survival analysis as well as univariate analysis and multivariate Cox analysis adjusted for age at diagnosis, pT stage, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, Ki-67 expression and human epidermal growth factor receptor 2 (HER-2) status.Results285 (85.8%) patients showed strong CD8+ CTLs infiltrate positive status. Univariate analysis showed that CD8+ CTLs had statistically significant association with DFS (P = 0.004, hazard ratio [HR] = 0.454, 95% confidence interval [CI] = 0.265–0.777) and OS (P = 0.014, HR = 0.430, 95% CI = 0.220–0.840) in the entire cohort. The significance of CD8+ CTLs was especially strong in ER negative, HER-2 negative and ER, PR, HER-2 triple-negative breast cancers. In Kaplan-Meier analysis, CD8+ CTLs had significant effect on prognosis of patients (Log-rank test: P = 0.003 for DFS and P = 0.011 for OS), independent of established clinical factors for DFS (P = 0.002, HR = 0.418, 95% CI = 0.242–0.724) as well as for OS (P = 0.009, HR = 0.401, 95% CI = 0.202–0.797).

Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills. To pre-train the question embeddings, we propose to use product-based neural networks to recover the side information. As a result, adopting the pre-trained embeddings in existing deep KT models significantly outperforms state-of-the-art baselines on three common KT datasets.

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.

Uniform core-shell heterostructured ZnWO 4 @MWO 4 (M ) Mn, Fe) nanorods with both optical and antiferromagnetic properties have been synthesized by a simple refluxing method under mild conditions in which the crystallization event of MWO 4 happened on the backbone of ZnWO 4 single crystalline nanorods in a ligand-free system. ZnWO 4 nanorod-directed oriented aggregation mechanism has been clearly observed for the formation of heterostructured ZnWO 4 @MWO 4 (M ) Mn, Fe) nanorods. The shell thickness of MWO 4 (M ) Mn, Fe) could be tuned by changing the molar ratio of these raw materials. UV-visible absorption spectra and photoluminescence (PL) spectra of the as-prepared ZnWO 4 @MWO 4 (M ) Mn, Fe) core-shell nanorods show the similar "red-shift" trend, which can be ascribed to the influences of the out-layered shell. The ZnWO 4 @MWO 4 (M ) Mn, Fe) nanorods displayed both optical and antiferromagnetic properties. The result demonstrated that the multifunctional anisotropic nanostructures with a heteroshell could be synthesized directly based on the oriented attachment mechanism.