Miracle-dz/UniFAR-FTUs huggingface dataset:
Timosaponin A‐III inhibits oncogenic phenotype via regulation of PcG protein BMI1 in breast cancer cells

Polycomb group (PcG) protein BMI1 is an important regulator of oncogenic phenotype and is often overexpressed in several human malignancies including breast cancer. Aberrant expression of BMI1 is associated with metastasis and poor prognosis in cancer patients. At present, therapy reagents that can efficiently inhibit the expression of BMI1 are not very well known. Here, we report that Timosaponin A‐III (TA‐III), a steroidal saponin obtained from the rhizomes of an herb, Anemarrhena asphodeloides, strongly inhibits expression of BMI1 in breast cancer cells. Treatment of breast cancer cells with TA‐III resulted in inhibition of oncogenic phenotypes such as proliferation, migration and invasion, and induction of cellular senescence. Inhibition of these oncogenic phenotypes was accompanied by downregulation of BMI1 expression and histone posttranslational modification activity of PRC1. The mechanistic analysis of TA‐III‐induced inhibition of oncogenic activity and BMI1 expression suggests that downregulation of c‐Myc mediates TA‐III effect on BMI1. We further show that exogenous BMI1 overexpression can overcome TA‐III‐induced inhibition of oncogenic phenotypes. We also show that TA‐III induces expression of tumor suppressive miR‐200c and miR‐141, which are negatively regulated by BMI1. In summary, our data suggest that TA‐III is a potent inhibitor of BMI1 and that it can be successfully used to inhibit the growth of tumors where PcG protein BMI1 and PcG activities are upregulated.
Extracting Generic Features of Artistic Style via Deep Convolutional Neural Network

While most existing works on image art style transformation generally focus on the transformation given a specific style image as input, in this paper, we consider it given a set of images of a generic style, e.g., images of Vincent van Gogh during 1889 to 1890. Compared to the specific style from only one input style image, our generic style transformation is able to remove the artifact generated from the single image such as specific objects and scenes. To this end, we propose a method to extract generic style features from a set of fine paintings. Generic style features describe these fine paintings from the global perspective, integrate features of brush strokes, color and pose contrast, scale information and orientation etc. We first obtain feature representation from these fine paintings using deep convolutional neural network (CNN), and then select generic representation from obtained representation. Finally, migrate visualized generic style features to input content image. Experimental results verify the efficiency and power of our method.
Dynamic modeling of robot based on neural network with incomplete state observations

This paper presents a novel dynamic modeling method of robot system using a recurrent neural network (RNN) with incomplete state variables observation. A dynamic model of a 2-DOF articulated robot is discussed, and the corresponding training method is deduced based on the back propagation through time (BPTT) algorithm. The effectiveness of this process is verified by simulation. The results show that the observed state variables are regressed, and the unobserved state variables are estimated.
Pressure–volume–temperature relationships of solid and molten polypropylene and poly(butene‐1)

Experimental data on the dependence of the specific volume on temperature and pressure (PVT data) to 2000 kg/cm2 of isotactic polypropylene and isotactic poly(butene-1) are reported and discussed. The temperature range covered is 30–297°C for polypropylene and 30–246°C for poly(butene-1), thus encompassing the solid and molten states of both materials. An empirical equation of state based on the Tait equation can be fitted to the melt data of both materials. The coefficients reported reproduce the measured specific volumes with a standard deviation of less than 0.001 cm3/g.
Dual region write buffering: making large-scale nonvolatile buffer using small capacitor in SSD

Write buffering not only enables Solid State Drive (SSD) to immediately respond to the write request but also increases the lifespan of the SSD by reducing the amount of data written to the flash memory. However, since the DRAM which is used as a write buffer is volatile, it has a few critical problems such as buffered data loss upon sudden power-off and limited write reduction effect due to the flush commands and synchronous writes from the file system. These problems can be addressed when non-volatile memory (NVRAM) is used, instead of DRAM, as a write buffer. In this paper, we propose a novel Dual-Region Write Buffering (DRWB) scheme that implements logically non-volatile write buffer using large sized DRAM and small capacity capacitor. The DRWB exploits the differential write scheme, which has been developed originally for the write reduction in SSD, to protect data in the write buffer. Experimental results show that the proposed scheme enables us to achieve the same effect with the NVRAM write buffer, in terms of the data reliability, without noticeable performance degradation.
Multiscale Modeling of Low Pressure Plasma Etching Processes: Linking the Operating Parameters of the Plasma Reactor with Surface Roughness Evolution

Α multiscale modeling framework, linking the operating parameters of a low pressure plasma reactor with the surface roughness being formed on the etched substrate, is developed. It consists of a) a reactor scale model, which calculates densities, energies, and fields in the reactor, b) a Monte Carlo (MC) particle tracing model, which calculates the ion energy and angular distributions on the substrate, and c) a MC surface model, which calculates the evolution of the surface morphology during etching. The case study is etching of polymeric substrates with Ar plasma in the GEC reactor. Based on a generic surface model, the effects of the operating conditions (pressure, power) on the roughness are investigated. The potential for non-uniform and anisotropic roughness along the wafer radial direction is demonstrated.
Investigation of hypoxia conditions using oxygen-enhanced magnetic resonance imaging measurements in glioma models

The objective of this study was to determine whether using oxygen-enhanced magnetic resonance imaging (OE-MRI) to assess hypoxia is feasible and whether historical measurements, pO2 changes, and percentage of signal intensity changes (PSIC) are correlated in an animal model of glioma. A total of 25 Sprague-Dawley rats were used to establish C6 brain or subcutaneous glioma model. Nine rats with brain gliomas underwent OE-MRI followed by histopathologic analysis to assess microvessel density and hypoxia. Another 11 rats were underwent OE-MRI and were followed for a survival analysis. Time–T1-weighted MR signal intensity (SI) curves and PSIC maps were derived from the OE-MRI data. High–regions of interests (ROI-h; PSIC > 10%) and low-ROIs (ROI-l; PSIC < 10%) were defined on the PSIC maps. To validate the PSIC map for identifying tumor hypoxia, we subjected an additional 5 rats with subcutaneous glioma to OE-MRI and pO2 measurements. All tumors showed regional heterogeneity on the PSIC maps. For the brain tumors, the time-SI curves for the ROIs-h showed a greater increase in SI than those for the ROIs-l did. The percentage of tumor area with a low PSIC was significantly correlated with the percentage of hypoxia staining and necrosis (r =0.71; P<0.05). ROIs with a higher PSIC typically had more vessels (r=0.88; P<0.05). A significant difference in survival was shown (log-rank P = 0.035). The time-pO2 curves of the subcutaneous tumors were similar to the time-SI curves. PSIC was significantly correlated with pO2 changes (r =0.82; P<0.05). These findings suggest that OE-MRI measurements can be used to assess hypoxia in C6 glioma models. In these models, the PSIC value was correlated with survival, indicating that PSIC could serve as a prognostic marker for glioma.
Cell‐specific resetting of mouse islet cellular clocks by glucagon, glucagon‐like peptide 1 and somatostatin

Molecular clocks, operative in pancreatic islet cells, represent an intrinsic mechanism regulating intracellular metabolism and hormone secretion. Glucagon, somatostatin and glucagon‐like peptide 1 (GLP‐1) are essential coordinators of islet physiology. Here, we assess the synchronizing capacity of glucagon, somatostatin and GLP‐1 on pancreatic α‐ and β‐cell circadian clocks.
Optimization of a rapid test by using fluorescein-conjugated monoclonal antibodies for detection of Chlamydia trachomatis in clinical specimens

A mixture of two fluorescein isothiocyanate-conjugated monoclonal antibodies (MAbs) was used to optimize a direct specimen test (Chlamydia Direct Specimen Test IF; Clonatec, Paris, France) for detection of chlamydial elementary bodies in clinical specimens. One MAb reacted with a subspecies-specific epitope of the major outer membrane protein (molecular weight 43,000) of Chlamydia trachomatis, whereas the other reacted with the periodate-sensitive genus-specific antigen (molecular weight 11,000) of Chlamydia spp. Nonfat dry milk was the most efficient additive at suppressing the fluorescent background and was included in the antibody preparation. Fc-dependent binding of fluorescein-conjugated MAbs to protein A-containing Staphylococcus aureus was inhibited by addition of purified rabbit immunoglobulin. The Chlamydia Direct Specimen Test IF was compared with tissue culture isolation by using 309 genital specimens. The sensitivity and specificity were 77.4 and 98%, respectively.
Prioritisation of food-borne parasites in Europe, 2016

Background and aims Priority setting is a challenging task for public health professionals. To support health professionals with this and in following a recommendation from the Food and Agriculture Organization of the United Nations (FAO) and World Health Organization (WHO), 35 European parasitologists attended a workshop from 8–12 February 2016 to rank food-borne parasites (FBP) in terms of their importance for Europe and regions within Europe. Methods: Countries were divided into European regions according to those used by the European Society of Clinical Microbiology and Infectious Diseases. We used the same multicriteria decision analysis approach as the FAO/WHO, for comparison of results, and a modified version, for better regional representation. Twenty-five FBP were scored in subgroups, using predefined decision rules. Results: At the European level, Echinococcus multilocularis ranked first, followed by Toxoplasma gondii and Trichinella spiralis. At the regional level, E. multilocularis ranked highest in Northern and Eastern Europe, E. granulosus in South-Western and South-Eastern Europe, and T. gondii in Western Europe. Anisakidae, ranking 17th globally, appeared in each European region’s top 10. In contrast, Taenia solium, ranked highest globally but 10th for Europe. Conclusions: FBP of importance in Europe differ from those of importance globally, requiring targeted surveillance systems, intervention measures, and preparedness planning that differ across the world and across Europe.
FPGA based image de-hazing architecture for real time applications

This paper proposes the design and Field Programmable Gate Array (FPGA) architecture of dark channel prior algorithm for image de-hazing. The RGB hazed image is read in the form of 3×3 patches (9 Pixels) and minimum intensity pixels are obtained from each patch. These minimum intensity pixels form a dark vector. Atmospheric light for each color is estimated using this dark vector. Transmission map is computed by atmospheric light. Guided filter is used to refine the transmission map and finally de-hazed image is recovered. The proposed architecture comprises of 6 cores and is implemented usingVerilog HDL and Xilinx ISE Design Suite 14.5. The proposed image de-hazing architecture is validated by comparing the simulation results with MATLAB results. The proposed architecture is synthesized for Xilinx Zynq board, Device XC7Z045, Package FFG900, Speed −3. Synthesis results show that proposed design consumes 24568 Slice registers and 18644 LUTs. Furthermore, the design consumes 71 BRAMs and can operate on Maximum Frequency of142.786MHzwith minimum clock period of 7.003ns.
A multi-criteria approach for assessing cloud deployment options based on non-functional requirements

Cloud computing is a recent computing paradigm that is changing software engineering. It offers scalable virtual compute resources at low prices, thus attracting many software developers interested in reducing their infrastructure and operational costs. Even though using cloud solutions is simple, with many providers and resource types available, a common difficulty developers face is how to best configure their applications using a myriad of cloud services, specially when considering different attributes such as cost, scalability, performance and others. A wrong architectural decision can lead to a significant cost increase or a deployment option that does not meet the minimum required performance. This work presents an approach that relies on non-functional requirements as key drivers for assessing and selecting, based on a multi-criteria optimization method, the best architectural options for deploying applications in the cloud. Results from a real application (WordPress) deployed in a popular cloud provider (Amazon) are discussed to illustrate the use and benefits of the approach.
A Novel Risk prediction Model for Patients with Combined Hepatocellular-Cholangiocarcinoma

Backgrounds: Regarding the difficulty of CHC diagnosis and potential adverse outcomes or misuse of clinical therapies, an increasing number of patients have undergone liver transplantation, transcatheter arterial chemoembolization (TACE) or other treatments. Objective: To construct a convenient and reliable risk prediction model for identifying high-risk individuals with combined hepatocellular-cholangiocarcinoma (CHC). Methods: 3369 patients who underwent surgical resection for liver cancer at Zhongshan Hospital were enrolled in this study. The epidemiological and clinical characteristics of the patients were collected at the time of tumor diagnosis. Variables (P <0.25 in the univariate analyses) were evaluated using backward stepwise method. A receiver operating characteristic (ROC) curve was used to assess model discrimination. Calibration was performed using the Hosmer-Lemeshow test and a calibration curve. Internal validation was performed using a bootstrapping approach. Results: Among the entire study population, 250 patients (7.42%) were pathologically defined with CHC. Age, HBcAb, red blood cells (RBC), blood urea nitrogen (BUN), AFP, CEA and portal vein tumor thrombus (PVTT) were included in the final risk prediction model (area under the curve, 0.69; 95% confidence interval, 0.51-0.77). Bootstrapping validation presented negligible optimism. When the risk threshold of the prediction model was set at 20%, 2.73% of the patients diagnosed with liver cancer would be diagnosed definitely, which could identify CHC patients with 12.40% sensitivity, 98.04% specificity, and a positive predictive value of 33.70%. Conclusions: Herein, the study established a risk prediction model which incorporates the clinical risk predictors and CT/MRI-presented PVTT status that could be adopted to facilitate the diagnosis of CHC patients preoperatively.
Towards a Decision Support System for Disorders of the Cardiovascular System - Diagnosing and Evaluation of the Treatment Efficiency

: The study describes a preliminary stage of the decision support system development for cardiovascular system disorders. As the clinical model of the disorders, the arterial hypertension was used. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recording while performing tilt-test functional load. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate a treatment efficiency. The results indicate the potential of the proposed decision support system.
Meanings of encounters for close relatives of people with a long-term illness within a primary healthcare setting

Abstract Background Encounters play an important role in the relationship between healthcare personnel and the close relatives of people with a long-term illness. Aim The aim of this study was to elucidate the meanings of encounters for close relatives of people with a long-term illness within a primary healthcare setting. Methods Interviews using a narrative approach were conducted with seven women and three men, and the phenomenological hermeneutic method was used to interpret the interview texts. Results The structural analysis revealed three major themes: being confirmed as a family, being informed of the care, and being respected as a valuable person. Close relatives stated that they wanted to be confirmed as a family and have a familiar and trusting relationship with healthcare personnel. They valued being informed concerning the care of the ill person so that they could give support at home. It was also important to be compassionately viewed as an important person in a welcoming atmosphere based on respect and dignity.
Decreased expression of LKB1 predicts poor prognosis in pancreatic neuroendocrine tumor patients undergoing curative resection

Background Liver kinase B1 (LKB1) is a key regulatory protein of cellular metabolism, proliferation, and polarity. The present study aimed to characterize the expression pattern of LKB1 in pancreatic neuroendocrine tumors (pNETs) and evaluate the relationship between LKB1 expression and prognosis in pNETs. Patients and methods We retrospectively analyzed the pathologic and clinical data of 71 pNET patients who underwent curative surgical resection in Guangdong General Hospital. LKB1 mRNA and protein levels in tumor tissues and paired nontumor tissues were evaluated in 24 patients by quantitative real-time reverse-transcription polymerase chain reaction and Western blot, respectively. Immunohistochemical expression of LKB1 in tumor tissues was detected in all of the 71 patients, and the immunohistochemical expression level was re-coded in two classes (high versus low/negative) and correlated with clinicopathological parameters and survival outcomes. The association between LKB1 expression and clinicopathological characters was evaluated by chi-square test and Student’s t-test. Kaplan–Meier curves and log-rank test were used to analyze the survival outcomes, including overall survival (OS) and disease-free survival (DFS). Results Compared to adjacent normal tissues, LKB1 mRNA level and protein expression level in tumor tissues were both increased. The immunostaining of LKB1 was mainly found within the cytoplasm. Overall, 52 of 71 (73.2%) cases were positive for LKB1 protein, which showed either a diffuse staining pattern or a partial staining pattern. Decreased LKB1 expression was correlated with older age (P=0.042), increased Ki-67 index (P=0.004), increased mitotic count (P=0.001), and advanced histologic grade (P=0.001). Moreover, patients with low/negative LKB1 expression had shorter OS and DFS than those with high expression. Conclusion Our results suggested that LKB1 expression could be a useful prognostic marker for recurrence and survival in pNET patients who had received curative resection.
Digital Image Processing Technique for Measurement of Coursed Deformation of Asphalt Pavement Core Samples

In order to evaluate the coursed deformation and mechanical property of core samples,a digital image processing technique is used for conventional uniaxial compression creep test of asphalt mixtures.Non-connect measuring process for measuring the coursed deformation of asphalt pavement core samples is realized.The shape and the deformation of the samples are automatically determined and recorded in real-time.This technique can be efficiently and conveniently applied to traditional creep tests apparatus.Accuracy and error of the measurement is analyzed,the effect of end restraint on the non-uniform deformation of samples is discussed,and measured results of core samples deformation are given for comparison between traditional method and the presented technique.In the uniaxial compression creep test of the cores,it is observed that the deformation of asphalt middle course is the maximum.The different loading history of cores results in the changes of material meso-structure and mechanical property.The deformation law of the highly compacted cores is different from the cores in shoulder,especially in creep failure step.
Design a soft assistive device for elbow movement training in peripheral nerve injuries

In recent years, robotic assistive devices have been widely used to improve the rehabilitation outcome of upper limb disability. Meanwhile in the peripheral nerve injuries disability, the brachial plexus injury (BPI) incidence has increased in developing countries which affects the quality of life for patients due to pain during insufficient arm movement of the patients who go under operative or conservative treatment. Pain will not stop the rehabilitation processes, but may lower the motivation of training. Our ultimate goal is to design a soft assistive device for elbow by developing wearable exoskeleton for promoting restoration of motor function with minimal pain. Pneumatic artificial muscles (PAMs) were applied to our prototype due to the properties which provide high power to weight ratio, light weight and simplicity to install. These actuators were regulated by a controller to perform elbow movements in flexion-extension, and pronation-supination. Furthermore, our work system also measured electromyography (EMG) and pain-related evoked potential (PREP) as estimation of user intended motion and pain feedback control, respectively. We use stimulation device with surface electrode which induced pinprick-like sensation to activate nociceptive fibers and recorded electroencephalography (EEG) on the scalp for measurement of pain during applied device with human arm. Therefore, this 2 degree of freedoms (DoF) assistive device can improve elbow movement while affect the least pain to the wearer.
Remote sensing ship detection technology based on DoG preprocessing and shape features

In order to detect ship targets in remote sensing images, we have made research on the detection in feature extraction and target detection. A method about Remote Sensing Ship Detection based on DoG Preprocessing and Shape Features was proposed. Firstly, the original image is processed by DoG filter, which made the blocks more obvious. Then, Otsu is used to segment the image after preprocessing, and a two-value result is obtained. Finally, we analyzed three types of the segmentation results are standardized including the density, the degree of the rectangle and the degree of circularity. The results of the experiment show that the proposed DoG preprocessing coupled with shape feature model can achieve 94.27% in accuracy, at the same time, the recall rate is as high as 87.70%, F1 was up to 90.87%. The research could provide important technical support for monitoring of the sea. It has important reference to the research of remote sensing target detection in the future.