bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enzalutamide response in a panel of prostate cancer cell lines reveals a role for glucocorticoid receptor in enzalutamide resistant disease
Authors: Smith Rebecca , Liu Moqing , Liby Tiera , Bayani Nora , Bucher Elmar , Chiotti Kami , Derrick Daniel , Chauchereau Anne , Heiser Laura , Alumkal Joshi , Feiler Heidi , Carroll Peter , Korkola James E. 
Published: December 2020

AbstractRepresentative in vitro model systems that accurately model response to therapy and allow the identification of new targets are important for improving our treatment of prostate cancer. Here we describe molecular characterization and drug testing in a panel of 20 prostate cancer cell lines. The cell lines cluster into distinct subsets based on RNA expression, which is largely driven by functional Androgen Receptor (AR) expression. KLK3, the AR-responsive gene that encodes prostate specific antigen, shows the greatest variability in expression across the cell line panel. Other common prostate cancer associated genes such as TMPRSS2 and ERG show similar expression patterns. Copy number analysis demonstrates that many of the most commonly gained (including regions containing TERC and MYC) and lost regions (including regions containing TP53 and PTEN) that were identified in patient samples by the TCGA are mirrored in the prostate cancer cell lines. Assessment of response to the anti-androgen enzalutamide shows a distinct separation of responders and non-responders, predominantly related to status of wild-type AR. Surprisingly, several AR-null lines responded to enzalutamide. These AR-null, enzalutamide-responsive cells were characterized by high levels of expression of glucocorticoid receptor (GR) encoded by NR3C1. Treatment of these cells with the anti-GR agent mifepristone showed that they were more sensitive to this drug than enzalutamide, as were several of the enzalutamide non-responsive lines. This is consistent with several recent reports that suggest that GR expression is an alternative signaling mechanism that can bypass AR blockade. This study reinforces the utility of large cell line panels for the study of cancer and identifies several cell lines that represent ideal models to study AR-null cells that have upregulated GR to sustain growth.
Return to different climate states by reducing sulphate aerosols under future CO2 concentrations
Authors: Takemura Toshihiko 
Published: December 2020

AbstractIt is generally believed that anthropogenic aerosols cool the atmosphere; therefore, they offset the global warming resulting from greenhouse gases to some extent. Reduction in sulphate, a primary anthropogenic aerosol, is necessary for mitigating air pollution, which causes atmospheric warming. Here, the changes in the surface air temperature under various anthropogenic emission amounts of sulphur dioxide (SO2), which is a precursor of sulphate aerosol, are simulated under both present and doubled carbon dioxide (CO2) concentrations with a climate model. No previous studies have conducted explicit experiments to estimate the temperature changes due to individual short-lived climate forcers (SLCFs) in different climate states with atmosphere–ocean coupled models. The simulation results clearly show that reducing SO2 emissions at high CO2 concentrations will significantly enhance atmospheric warming in comparison with that under the present CO2 concentration. In the high latitudes of the Northern Hemisphere, the temperature change that will occur when fuel SO2 emissions reach zero under a doubled CO2 concentration will be approximately 1.0 °C, while this value will be approximately 0.5 °C under the present state. This considerable difference can affect the discussion of the 1.5 °C/2 °C target in the Paris Agreement.
Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$
Authors: Das Soumya Ranjan , Wakabayashi Katsunori , Tsukagoshi Kazuhito , Dutta Sudipta 
Published: December 2020

AbstractTriangular growth patterns of pristine two-dimensional (2D) transition metal dichalcogenides (TMDs) are ubiquitous in experiments. Here, we use first-principles calculations to investigate the growth of triangular shaped oxide islands upon layer-by-layer controlled oxidation in monolayer and few-layer $$\hbox {WSe}_{2}$$

WSe
2

systems. Pristine 2D TMDs with a trigonal prismatic geometry prefer the triangular growth morphology due to structural stability arising from the edge chalcogen atoms along its three sides. Our ab-initio energetics and thermodynamic study show that, since the Se atoms are more susceptible to oxygen replacement, the preferential oxidation happens along the Se zigzag lines, producing triangular islands of transition metal oxides. The thermodynamic stability arising from the preferential triangular self-formation of TMD based oxide heterostructures and their electronic properties opens a new avenue for their exploration in advanced electronic and optoelectronic devices.
Trends and risk factors for infant mortality in the Lao People’s Democratic Republic
Authors: Louangpradith Viengsakhone , Yamamoto Eiko , Inthaphatha Souphalak , Phoummalaysith Bounfeng , Kariya Tetsuyoshi , Saw Yu Mon , Hamajima Nobuyuki 
Published: December 2020

AbstractA high infant mortality rate (IMR) indicates a failure to meet people’s healthcare needs. The IMR in Lao People’s Democratic Republic has been decreasing but still remains high. This study aimed to identify the factors involved in the high IMR by analyzing data from 53,727 live births and 2189 women from the 2017 Lao Social Indicator Survey. The estimated IMR decreased from 191 per 1000 live births in 1978–1987 to 39 in 2017. The difference between the IMR and the neonatal mortality rate had declined since 1978 but did not change after 2009. Factors associated with the high IMR in all three models (forced-entry, forward-selection, and backward-selection) of multivariate logistic regression analyses were auxiliary nurses as birth attendants compared to doctors, male infants, and small birth size compared to average in all 2189 women; and 1–3 antenatal care visits compared to four visits, auxiliary nurses as birth attendants compared to doctors, male infants, postnatal baby checks, and being pregnant at the interview in 1950 women whose infants’ birth size was average or large. Maternal and child healthcare and family planning should be strengthened including upgrading auxiliary nurses to mid-level nurses and improving antenatal care quality.
Analysis of the stability of 70 housekeeping genes during iPS reprogramming
Authors: Panina Yulia , Germond Arno , Watanabe Tomonobu M. 
Published: December 2020

AbstractStudies on induced pluripotent stem (iPS) cells highly rely on the investigation of their gene expression which requires normalization by housekeeping genes. Whether the housekeeping genes are stable during the iPS reprogramming, a transition of cell state known to be associated with profound changes, has been overlooked. In this study we analyzed the expression patterns of the most comprehensive list to date of housekeeping genes during iPS reprogramming of a mouse neural stem cell line N31. Our results show that housekeeping genes’ expression fluctuates significantly during the iPS reprogramming. Clustering analysis shows that ribosomal genes’ expression is rising, while the expression of cell-specific genes, such as vimentin (Vim) or elastin (Eln), is decreasing. To ensure the robustness of the obtained data, we performed a correlative analysis of the genes. Overall, all 70 genes analyzed changed the expression more than two-fold during the reprogramming. The scale of this analysis, that takes into account 70 previously known and newly suggested genes, allowed us to choose the most stable of all genes. We highlight the fact of fluctuation of housekeeping genes during iPS reprogramming, and propose that, to ensure robustness of qPCR experiments in iPS cells, housekeeping genes should be used together in combination, and with a prior testing in a specific line used in each study. We suggest that the longest splice variants of Rpl13a, Rplp1 and Rps18 can be used as a starting point for such initial testing as the most stable candidates.
Processing of pragmatic communication in ASD: a video-based brain imaging study
Authors: Kotila Aija , Hyvärinen Aapo , Mäkinen Leena , Leinonen Eeva , Hurtig Tuula , Ebeling Hanna , Korhonen Vesa , Kiviniemi Vesa J. , Loukusa Soile 
Published: December 2020

AbstractSocial and pragmatic difficulties in autism spectrum disorder (ASD) are widely recognized, although their underlying neural level processing is not well understood. The aim of this study was to examine the activity of the brain network components linked to social and pragmatic understanding in order to reveal whether complex socio-pragmatic events evoke differences in brain activity between the ASD and control groups. Nineteen young adults (mean age 23.6 years) with ASD and 19 controls (mean age 22.7 years) were recruited for the study. The stimulus data consisted of video clips showing complex social events that demanded processing of pragmatic communication. In the analysis, the functional magnetic resonance imaging signal responses of the selected brain network components linked to social and pragmatic information processing were compared. Although the processing of the young adults with ASD was similar to that of the control group during the majority of the social scenes, differences between the groups were found in the activity of the social brain network components when the participants were observing situations with concurrent verbal and non-verbal communication events. The results suggest that the ASD group had challenges in processing concurrent multimodal cues in complex pragmatic communication situations.
Rapid and sensitive detection of SARS-CoV-2 antibodies by biolayer interferometry
Authors: Dzimianski John V. , Lorig-Roach Nicholas , O’Rourke Sara M. , Alexander David L. , Kimmey Jacqueline M. , DuBois Rebecca M. 
Published: December 2020

AbstractSerological testing to evaluate antigen-specific antibodies in plasma is generally performed by rapid lateral flow test strips that lack quantitative results or by high complexity immunoassays that are time- and labor-intensive but provide semi-quantitative results. Here, we describe a novel application of biolayer interferometry for the rapid detection of antigen-specific antibody levels in plasma samples, and demonstrate its utility for quantification of SARS-CoV-2 antibodies. Our biolayer interferometry immunosorbent assay (BLI-ISA) utilizes single-use biosensors in an automated “dip-and-read” format, providing real-time optical measurements of antigen loading, plasma antibody binding, and antibody isotype detection. Complete semi-quantitative results are obtained in less than 20 min. BLI-ISA meets or exceeds the performance of high complexity methods such as Enzyme-Linked Immunosorbent Assay (ELISA) and Chemiluminescent Immunoassay. Importantly, our method can be immediately implemented on existing BLI platforms for urgent COVID-19 studies, such as serosurveillance and the evaluation of vaccine candidates. In a broader sense, BLI-ISA can be developed as a novel diagnostic platform to evaluate antibodies and other biomolecules in clinical specimens.
Intelligent reflecting surface‐assisted single‐input single‐output Golden codeword‐based modulation schemes
Authors: Pillay Narushan , Xu HongJun 
Published: December 2020

AbstractIn this paper, two intelligent reflecting surface (IRS)‐assisted single‐input single‐output Golden codeword‐based modulation schemes for next‐generation networks are investigated. In Scheme 1, one pair of Golden codeword super‐symbols are transmitted over two consecutive time slots with the assistance of an IRS to maximise the received signal‐to‐noise ratio (SNR) in each slot. Scheme 2 applies component‐interleaving to the pair of Golden codeword super‐symbols and transmits the modified super‐symbols over four consecutive time slots with the assistance of an IRS to maximise the received SNR in each slot. A significant improvement in error performance is demonstrated over their counterparts, for both conventional/non‐access point (AP) or AP‐based IRSs. Scheme 2 exhibits superior error performance over Scheme 1, especially for small and medium IRSs. The theoretical average bit error probabilities for the proposed schemes are formulated and validated by simulation results. Low‐complexity detectors for the schemes under study are provided and the computational complexity imposed by these detectors are analysed and found to be extremely low.
Finite‐time adaptive neural dynamic surface control for non‐linear systems with unknown dead zone
Authors: Chen Lian , Wang Qing 
Published: December 2020

AbstractThis paper proposes a new adaptive neural network finite‐time dynamic surface control scheme for inaccurate non‐linear systems subjected to unknown dead zones. The ‘explosion of differentiation’ is eliminated by the first‐order filter in backstepping design. The parameter normalization scheme updates the coefficients of activation functions in the radial basis function neural networks. The dead zone inverse method estimates the dead zone parameters. It is proved that the proposed controller achieves the faster tracking performance and the boundedness of all signals in finite time. The contribution of this paper is that the presented controller not only has the advantages of transient performance and program execution time in comparison with traditional backstepping methods, but also its computational cost is lower than command filtered methods. Simulation experiment results are included to illustrate the effectiveness of the proposed scheme.
Segmentation of natural images based on super pixel and graph merging
Authors: Mukherjee Aritra , Sarkar Soumik , Saha Sanjoy K. 
Published: December 2020

AbstractThe task of natural image segmentation is one of the most researched topics of computer vision. There are mainly two principal approaches for the task, the statistical approach and the supervised approach. The proposed methodology segments natural images combining a set of statistical algorithms. First, the image is preprocessed to enhance the edges. Weighted average of the denoised image and its derivatives is the preprocessed output. Thereafter, an energy based super pixelation is applied to over segment the image. Finally, a connectivity graph is built where nodes correspond to super pixels and edges connect the adjacent super pixels. The adjacent super pixels are merged based on the confidence value defined in terms of their textural and colour similarity. Proposed methodology has been applied on the images of BSDS500 dataset. Performance of the proposed work has been compared with that of other works based on detected edge maps. Few works generate ultrametric contour maps (UCM). To compare the performance with those works, UCM is also generated by the proposed methodology. To do so images at multiple scales are considered. It is observed that the output of segmentation is better in case of the proposed methodology. Proposed methodology is much faster than others. Thus, makes it suitable for real time application in robot vision.
Exponential to ZIP and ZIP to exponential load model conversion: Methods and error
Authors: Leinakse Madis , Kilter Jako 
Published: December 2020

AbstractThis paper presents several methods for performing two types of static load model conversion: exponential to ZIP & ZIP to exponential model conversion. In general, these conversions are inaccurate due to non‐equivalence of exponential and ZIP (second‐order polynomial) models. A numerical analysis is conducted using generated datasets of load models to analyse the error and to compare the accuracy of the presented methods. The results of the analysis indicate that the optimal selection of conversion method depends on a number of factors, including normalisation of load models, ZIP model type (accurate or constrained) and expected use of the converted model. In addition, a case study is conducted to analyse the impact of conversion error on load flow results. The results of the case study indicate that a significant difference in load flow results can occur when the load models are converted. Recommendations for conversion method selection are given in the discussion section of the paper.
An efficient semantic segmentation method based on transfer learning from object detection
Authors: Yang Wei , Zhang Jianlin , Chen Zhongbi , Xu Zhiyong 
Published: December 2020

AbstractNowadays, numerous semantic segmentation techniques were used to complex scenes such as urban streets. However, speed issues are not considered in most of these methods, and real‐time methods do not mainly include enough accuracy. In this paper, an efficient semantic segmentation method is proposed, using the feature extractor of a real‐time object detection model, Darknet53, as the backbone of DeepLabv3+. By the high accuracy of DeepLabv3+ structure and great efficiency of Darknet53, a mean intersection was obtained over union of 76.3% in Cityscapes test set, and fast inference speed simultaneously (0.178 s per frame on one GTX 1080Ti GPU). A huge imbalance of objects was noticed on Cityscapes dataset. To solve this problem, a Focal Loss like loss function was proposed to concentrate more on the hard difficult pixels. Moreover, an atrous convolution block was proposed to extract more high‐level features. Based on the experimental results, it is proved that these changes contribute to a better result on the Cityscapes test set (77.8% mean Intersection over Union) and faster inference speed (0.171 s per frame). Authors' model achieves state‐of‐art results on Cityscapes test set (79.1% mean Intersection over Union) after fine‐tuning on Cityscapes coarsely annotated dataset.
Interest point detection from multi‐beam light detection and ranging point cloud using unsupervised convolutional neural network
Authors: Yin Deyu , Zhang Qian , Liu Jingbin , Liang Xinlian , Wang Yunsheng , Chen Shoubin , Maanpää Jyri , Hyyppä Juha , Chen Ruizhi 
Published: December 2020

AbstractInterest point detection plays an important role in many computer vision applications. This work is motivated by the light detection and ranging odometry task in autonomous driving. Existing methods are not capable of detecting enough interest points in unstructured scenarios where there are little constructions or trees around, and correspondingly light detection and ranging odometry will fail to continuous localisation. An interest point detector is proposed for detecting interest points from multi‐beam light detection and ranging point cloud using unsupervised convolutional neural network. The point cloud is projected into a two‐dimensional structured data according to the scanning geometry. Then the convolutional neural network filters trained in an unsupervised manner are used to generate a local feature map with the two‐dimensional structured data as input. Finally, interest points are obtained by extracting the grids that have significant differences with their neighbour grids. Based on an odometry benchmark, the experiments show that the proposed interest point detector can capture more local details, which contributes to more than 16% error decrease in point cloud registration in highway scenes.
A feature‐optimized Faster regional convolutional neural network for complex background objects detection
Authors: Wang Kun , Liu Maozhen 
Published: December 2020

AbstractIn recent years, convolutional neural networks are playing an increasingly important role in the field of object detection. However, the complex background of the detected image, the limited receptive field by the fixed geometry of the convolution kernel when building the model, and the positioning and pooling deviation from the region of interest are still important factors that affect the detection accuracy. In this paper, an improved algorithm is proposed for target detection based on Faster regional convolutional neural network. In the bounding box positioning phase, an improved interpolation algorithm‐Newton's parabolic interpolation is proposed instead of bilinear interpolation, after ROI size normalized by extending a parallel branch of tensor to weaken the negative impact of complex background on prospects in the phase of feature extraction using neural network recently popular attention CBAM mechanism model and the deformable convolution. Without bells and whistles, a series of experiments show that our method has higher target detection accuracy on the datasets PASCAL VOC2007, VOC2012, COCO 2014 and DIOR. Hence, the method is effective for actual target recognition tasks in complex background environments. The authors hope that the method will contribute to future research. Code has been made available at: https://github.com/liumaozhen‐lmz/Faster_R‐CNN_Attention.git.
Vehicle detection in aerial images based on lightweight deep convolutional network
Authors: Shen Jiaquan , Liu Ningzhong , Sun Han 
Published: December 2020

AbstractVehicle detection in aerial images is an interesting and challenging task. Traditional methods are based on sliding‐window search and handcrafted features, which limits the representation power and has heavy computational costs. Recent research have shown that deep‐learning algorithms are widely used in the field of object detection. However, the deep‐learning algorithms still face many difficulties and challenges in the object detection under the aerial scene. Meanwhile, the high computational cost of detection algorithms lead to low‐detection efficiency. In this study, we build a fast and accurate lightweight detection framework for vehicle detection in aerial scenes. The proposed detection method improves the expressive ability of detection network and significantly reduces the amount of calculations in the model. Meanwhile, setting suitable anchor boxes according to the size of the object vehicles have been introduced in our model, which also effectively improves the performance of the detection. In addition, we have published a new aerial vehicle image dataset and verified the effectiveness of our method. In the Munich dataset and our dataset, our method achieves 85.8% and 91.2% of the mean average precision (mAP), and its detection time is 1.78 and 0.048 s on Nvidia Titan XP. Our results show that the proposed framework achieves significant improvement over several alternatives and state‐of‐the‐art schemes with higher accuracy and less detection time.
Electrical properties evaluation of double‐layer nano‐filled oil‐paper composites
Authors: Abd‐Elhady Amr M. , Abdul‐Aleem Ahmed A. , Izzularab Mohamed A. 
Published: December 2020

AbstractOil‐paper composite is the main insulation system in oil‐filled transformers. In recent years, the use of nanotechnology to improve the electrical and thermal properties of transformer oil has become a field of research interest. In this paper, the electrical properties such as breakdown strength, volume resistivity, and dielectric response of nano‐filled oil‐paper insulation are evaluated. The evaluation is carried out on a double‐layer of oil‐paper composites insulation. The double‐layer samples are prepared considering different nano‐filler types. The studied nano‐filler types are Pb3O4, Al2O3, and SiO2 with average particle size 25 nm of each. For each nano‐filler type, oil‐paper samples with different concentrations (0.04, 0.06, 0.1, 0.2, and 0.4 g/L) are prepared. The effect of nano‐filler types on the breakdown strength, volume resistivity, and dielectric response of oil‐paper composites are highly evaluated. The results show that the filler types as well as filler permittivity affects the breakdown strength, volume resistivity, and dielectric response of oil‐paper composites insulation. Also, the results show that nano‐filler with high permittivity gives higher improvement in breakdown strength of Oil‐paper composite compared nano‐filler with low permittivity. Finally, the interpretation of all obtained results considering breakdown strength, volume resistivity, and dielectric response is highly reported.