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Background: The x-ray repair cross-complementing (XRCC) family is essential in DNA repair processes. The predictive roles of XRCCs remain unclear in ovarian carcinomas. Therefore, detecting the relationship between XRCCs expression and ovarian carcinomas prognosis is increasingly pivotal. Methods: Using the "Kaplan-Meier (KM) plotter" database, progression-free survival (PFS) and overall survival (OS) were utilized to evaluate the prognosis of XRCCs mRNA expression in ovarian carcinoma patients with clinical outcomes. Then, mRNA level and protein levels of XRCCs were assessed in normal ovarian cells and ovarian carcinoma cell lines by real-time qPCR, Western blotting and immunofluorescence analysis. Additionally, expression of the XRCCs protein in tissues from ovarian carcinomas and normal ovary was identified by immunohistochemical staining. Results: Higher mRNA levels of XRCC2 and XRCC9 predicted longer PFS and OS in all women with ovarian malignance, while elevated XRCC4 mRNA levels were linked to poor PFS and OS in all ovarian cancer patients. Elevated mRNA of XRCC2 was also correlated with better PFS in patients with serous ovarian carcinomas, and better PFS and OS in grade III and stage III+IV ovarian carcinomas patients. What's more, highly expressed levels of XRCC9 mRNA were also linked to favorable PFS and OS in patients with serous, grade III and stage III+IV ovarian carcinomas. Nevertheless, elevated mRNA expression of XRCC4 was linked to worse PFS and OS for patients with serous, grade III as well as all stages of ovarian malignance. Additionally, when compared to ovarian carcinoma cell lines, elevated mRNA and protein levels of XRCC2 and XRCC9 were detected in normal ovarian cells. Consistently, higher staining of XRCC2 and XRCC9 was also detected in normal ovarian cells than that in ovarian cancer cells. Then, higher staining levels of XRCC2 and XRCC9 were discovered in healthy control tissues than that in ovarian carcinoma tissues. Meanwhile, XRCC4 was identified to be overexpressed in tissues of ovarian malignance as compared to normal control tissues. However, XRCC4 mRNA and protein levels were lower in ovarian cancer cells than that in normal cell line. Conclusion: Elevated XRCC2 and XRCC9 expression levels were observed in normal ovarian cells and tissues than that in ovarian malignance cells and tissues, and exhibited better prognostic value especially in patients with serous, poor differentiated and late stage, suggesting that XRCC2 and XRCC9 may be potent prognostic markers in ovarian cancer patients and can guide personalized surveillance for ovarian malignance.

Study Design: Retrospective study. Objective: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain. Method: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method. Results: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP ( P = .017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1. Conclusion: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment.

In the COVID-19 crisis, multilingual and multimodal translation services, provided by both professional translators and volunteer communities, have greatly facilitated the communication between different parties. This chapter gives an overview of how diverse agents have engaged in this large-scale translation endeavor and draws attention to the importance of translation planning in crisis contexts. It not only discusses the operations of volunteer translators, highlighting their contribution in providing language assistance in crisis situations, but also analyzes the significant role played by government-sponsored institutions in mobilizing language professionals and organizing translation projects, such as Language Service Group Against COVID-19 and China International Publishing Group. Moreover, this chapter focuses on the relevance of translation to waixuan (often translated as international publicity) in this crisis situation and investigates how translation is strategically adopted by the government as a medium of communicating the Chinese experience of fighting COVID-19. After analyzing all these disparate efforts, it argues for the significance of translation in overcoming language barriers in crisis contexts and concludes with suggestions on how to enhance translation planning for health emergencies.

The applications of extreme learning machine (ELM) to the hyperspectral-image (HSI) classification have attracted a great deal of research attention because of its excellent performance and fast learning speed. However, conventional ELM is unable to achieve satisfactory accuracy since it only exploits the spectral information to conduct the HSI classification. To address the above issues, we propose a novel classification algorithm based on both spectral and multiscale spatial information, referred to as ELM with enhanced composite feature (ELM-ECF). To be specific, we adopt the original ELM, exploit a multiscale spatial weighted-mean-filtering-based approach to extract multiple spatial information, and use the majority vote method to select the final classification result. The proposed ELM-ECF significantly improves the classification accuracy of the original ELM. Experimental results on three public HSIs (i.e., Indian Pines data set, Pavia University data set, and Salinas data set) illustrate that the proposed ELM-ECF outperforms a variety of the state-of-the-art HSI classification counterparts in terms of classification accuracy.

As perovskite solar cell (PSC) technology is about to be commercialized, the use of toxic and organic material is still a problem. At the same time, hole-transport-layer-free (HTL-free) PSCs have received widespread attention given their simple structure and low manufacturing cost. In this study, we use the wxAMPS software to produce a simulation, using C s S n I 3 ( S n - r i c h ) / C s S n I 3 all-perovskite as the absorption layer and carbon as the back electrode, as well as to propose a new, to the best of our knowledge, inorganic HTL-free PSC. C s S n I 3 is a narrowband material whose absorption range can be extended to the near-infrared spectral region; it also has very high hole mobility and so can be used as both the light absorption layer and the HTL. Compared with that of standard structure devices, the photoelectric conversion efficiency (PCE) of the PSC is better. When the perovskite layer thickness is 900 nm, the optimal PCE is 14.91%, the open-circuit voltage is 0.81 V, the short-circuit current is 23.77 m A / c m 2 , and the fill factor is 77.37%. Our research further found that C s S n I 3 (Sn-rich) has the best PCE at 10 18 ∼ 10 19 / c m 3 . When the electron transport layer and the light absorption layer conduction band offset is − 0.2 e V , the PCE has an optimal value of 15.02%. Finally, by changing the light temperature, the PCE value of our study is between 12.07% and 16.61% under non-extreme work environments, and the results show that the device has good thermostability. This proves that the proposed all-inorganic HTL-free PSCs have broad prospects in future photovoltaic and optoelectronics applications and provide theoretical guidance for the manufacture of non-toxic and inorganic PSCs.

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program "e-Bitter" is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method., 13 pages,8 figures,3 tables,article

As a new machine learning method, extreme learning machine (ELM) has received wide attention for image classification due to its good performances. Since ELM cannot catch the spatial information, the results of classification is not good while applying to hyperspectral image (HSI) classification. In view of this, this paper proposes a new method for HSI classification by combining ELM with Loopy Belief Propagation (LBP). The proposed method can not only reduce time-consuming, but also improve the accuracy of classification greatly. The experimental result in HSI data set of Indian Pines demonstrates that the proposed method outperforms several classical algorithms.

The system uses the ultra-low power MSP430F449 MCU as the control core, and uses the infrared wireless communication, achieving a simple underwater wireless communication. The entire system consists of 3 parts, the land-based unit, the electrical unit and the submarine model. The land-based unit uses the ASK modulation to achieve wireless communication with the submarine model. The submarine model receives and decodes signals, then communicates with model unit via infrared light waves .The motor unit receives the infrared signals and keeps the floating and diving of the submarine model by controlling the suspension. The system realizes the controlling of the submarine model to achieve various forms of movement when the antenna is 3 meters above the water. The distance error, time error and speed error all meet the requirements .The whole system operates simply and functions well.