text:
Precision Medicine and Imaging
Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening
Sarah S. Aboutalib, Aly A. Mohamed, Wendie A. Berg, Margarita L. Zuley, Jules H. Sumkin and Shandong Wu
Sarah S. Aboutalib
Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
ORCID record for Sarah S. Aboutalib
Aly A. Mohamed
Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Wendie A. Berg
Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.Magee-Womens Hospital of University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
Margarita L. Zuley
Jules H. Sumkin
Shandong Wu
Departments of Radiology, of Biomedical Informatics, of Bioengineering, and of Intelligent Systems, University of Pittsburgh, Pittsburgh, Pennsylvania.
For correspondence: wus3@upmc.edu
DOI: 10.1158/1078-0432.CCR-18-1115 Published December 2018
Purpose: False positives in digital mammography screening lead to high recall rates, resulting in unnecessary medical procedures to patients and health care costs. This study aimed to investigate the revolutionary deep learning methods to distinguish recalled but benign mammography images from negative exams and those with malignancy.
Experimental Design: Deep learning convolutional neural network (CNN) models were constructed to classify mammography images into malignant (breast cancer), negative (breast cancer free), and recalled-benign categories. A total of 14,860 images of 3,715 patients from two independent mammography datasets: Full-Field Digital Mammography Dataset (FFDM) and a digitized film dataset, Digital Dataset of Screening Mammography (DDSM), were used in various settings for training and testing the CNN models. The ROC curve was generated and the AUC was calculated as a metric of the classification accuracy.
Results: Training and testing using only the FFDM dataset resulted in AUC ranging from 0.70 to 0.81. When the DDSM dataset was used, AUC ranged from 0.77 to 0.96. When datasets were combined for training and testing, AUC ranged from 0.76 to 0.91. When pretrained on a large nonmedical dataset and DDSM, the models showed consistent improvements in AUC ranging from 0.02 to 0.05 (all P > 0.05), compared with pretraining only on the nonmedical dataset.
Conclusions: This study demonstrates that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish recalled-benign images from malignant and negative cases, which may lead to a computerized clinical toolkit to help reduce false recalls.
Translational Relevance
Breast cancer screening mammography is currently affected by high false recall rates resulting in unnecessary stress to patients, increased medical costs, and increased clinical workload. Deep learning convolutional neural networks (CNN) can be used to recognize the nuanced imaging features that distinguish recalled but benign mammography images, which may not be identifiable by human visual assessment. These imaging features can then be used for imaging interpretation and lead to computational tools that aid radiologists in distinguishing these images and thus help in the reduction of the false recall rate. In addition, with the ability of the CNN model to distinguish between negative and malignant images, deep learning can also perform well in computer-aided diagnosis of breast cancer.
Mammography is clinically used as the standard breast cancer screening exam for the general population and has been shown effective in early detection of breast cancer and in reduction of mortality (1–3). High recall (asking a woman back for additional workup after a screening mammogram) rates are, however, a concern in breast cancer screening. On average, approximately 11.6% of women in the U.S. (over 5 million women annually) who are screened using digital mammography are recalled and over 70% of more than 1 million breast biopsies performed annually are benign (4, 5), resulting in unnecessary psychological stress, medical costs, and clinical workload. Thus, reducing false recalls from screening mammography is of great clinical significance.
Observer performance in breast cancer detection in screening mammography varies widely and is influenced by experience, volume, and subspecialization among other factors (6). One approach to improving mammographic interpretation is to provide the radiologist with powerful computerized tools to aid in image interpretation and decision-making. To help radiologists make more accurate decisions on whether to recall a patient, we focused on building computer-aided models/classifiers that can distinguish subtle imaging characteristics of mammograms from patients who were recalled but biopsied with benign results from mammograms read as negative from the onset, and mammograms from recalled cases with biopsy-proven breast cancer. Computerized classifiers can assist radiologists in predicting which patients/images may be recalled but most likely benign.
Deep learning (7) coupled with a big dataset has shown promising performance in many artificial intelligence applications and is entering the field of biomedical imaging (8–10). The main architecture of deep learning for image data is the convolutional neural network (CNN; ref. 11). The most distinguishing strength of the CNN is that it can automatically learn and hierarchically organize features from a large dataset without manual feature engineering (7, 12, 13) and outperformed, in many scenarios, traditional manual-engineered imaging features/descriptors. Although deep learning CNN can be used as an offline feature-extractor where features are coupled with traditional classifiers (14), it is more common to build end-to-end deep learning classification models (15, 16). Studies applying deep learning to digital mammography images have focused on mass and microcalcification detection and classification, distinction between tumor and benign/normal tissue (17–21), breast tissue segmentation (22), and classification of breast anatomy (9).
Recently, a number of groups competed under the Digital Mammography DREAM challenge (23) to distinguish malignant from negative mammogram images. While important, distinguishing potentially recalled but biopsy benign images from both malignant and negative images represents a critical need and a practical approach to aid radiologists. The purpose of this study was to investigate end-to-end deep learning CNN models for automatic identification of nuanced imaging features to distinguish mammogram images belonging to negative, recalled-benign, and malignant cases aimed to improve clinical mammographic image interpretation and reduce unnecessary recalls.
We performed a retrospective study that received Institutional Review Board approval at our institution. This study was compliant to the Health Insurance Portability and Accountability Act and the U.S. Common Rule. Informed consent from patients was waived due to the retrospective nature.
Study cohort and datasets
We used two independent mammography datasets to develop and evaluate deep learning classifiers. These two independent mammogram datasets include a total of 3,715 patients and 14,860 images. A patient case typically contains a single patient exam with the standard four screening mammography views including left and right breast with craniocaudal (CC) and mediolateral oblique (MLO) views. Malignant images were taken from patients determined to have breast cancer based on the pathology results (only images of the cancer-affected breast were used). Negative images were from those who maintained a breast cancer–free status after at least a one-year follow-up. Recalled-benign images were taken from patients who were recalled based on the screening mammography exam but later determined as benign (biopsy-proven or diagnostically confirmed).
Full-field Digital Mammography dataset.
This is a retrospective cohort of 1,303 patients (5,212 mammogram images) who underwent standard digital mammography screening (2007–2014) at our institution: 552 patients were evaluated as negative in the initial screen; 376 patients were recalled-benign; 375 patients were evaluated as positive for breast cancer (101 or 26.9% ductal carcinoma in situ and 274 or 73.1% invasive) based on pathology results.
Digital Database of Screening Mammography dataset.
The Digital Database of Screening Mammography (DDSM) dataset (24–26) is a large collection of digitized film mammography images. A total of 9,648 images consisting of 2,412 patient cases were used from this dataset. Six-hundred and ninety-five cases were negative, 867 malignant, and 850 recalled-benign.
Deep learning approach for building classifiers
We built end-to-end two-class and three-class CNN models to investigate six classification scenarios: Five binary classifications: malignant versus recalled-benign + negative (i.e., recalled-benign and negative are merged into one class), malignant versus negative, malignant versus recalled-benign, negative versus recalled-benign, and recalled-benign versus malignant + negative (negative and malignant are merged into one class), as well as one triple classification: malignant versus negative versus recalled-benign.
The CNN used a modified version of the AlexNet (11, 27) model. The CNN structure consists of five convolutional layers (includes max-pooling in the 1st, 2nd, and 5th convolutional layers) followed by two fully connected layers and a fully connected output layer with a final softmax function. CNN model parameters were fixed in all experiments: batch size of 50 for stochastic gradient descent, a weight decay of 0.001, and a momentum of 0.9. For the learning rate, we started with 0.001 and dropped the rate by a factor of 10 every 2,500 iterations. To speed up training, rectified linear units were used as the activation function in place of traditional tangent sigmoid functions (7). To maximize performance and increase computational efficiency of the AlexNet network, images were preprocessed using standard techniques including histogram equalization, mean subtracting, and downsampling using standard bicubic interpolation to 227 × 227 pixels from original resolution.
Transfer learning was used to enhance model training. We pretrained all the CNN models with a large existing image dataset [ImageNet (28), 1.3 million nonmedical images] and then fine-tuned the models with our own mammography dataset. In a novel approach, we also tested the incremental transfer learning strategies: first pretraining the network using ImageNet and then continuing with the DDSM dataset, and finally fine-tuning and testing on the Full-field Digital Mammography (FFDM) dataset.
The CNN was trained with the goal of increasing variation of the data and avoiding overfitting. Internal validation is based on 6-fold cross validation of training data in the CNN model training phase, reducing overfitting and calibrating accuracy of the training process. External evaluation of the CNN models was performed using unseen testing data.
The deep learning network was implemented using the Caffe platform running on a desktop computer system with the following specifications: Intel Core i7-2670QM CPU@2.20GHZ with 8 GB RAM and a Titan X Pascal Graphics Processing Unit (GPU).
Evaluation and statistical analysis
An independent test cohort consisting of approximately 5% of our datasets was used for testing (95% data for training). We used this setting to maximize the amount of data for CNN model training. Table 1 summarizes the number of images used in our independent training and testing datasets in each experiment. As an additional robustness analysis, we also repeated the experiments by using 10% and 15% of the data as independent sets for testing. In all settings, testing data did not include any images used in training.
Number of training and testing images from the FFDM and digital DDSM datasets used for each experiment. The negative versus recalled-benign scenarios (negative vs. recalled-benign) have more data and thus are listed separately. For simplicity, all other scenarios (malignant vs. negative + recalled-benign, malignant vs. negative, malignant vs. recalled-benign, malignant vs. negative vs. recalled-benign, recalled-benign vs. malignant + negative) are listed under "Others." Both the total number of images in the scenario as well as the images per category are displayed.
We performed four different experiments/strategies utilizing the two datasets in testing the CNN model's performance in six classification scenarios. These four experiments included: (i) using the FFDM dataset only for training and testing; (ii) using the DDSM dataset only for training and testing, in order to determine how our CNN model performs on an independent dataset; (iii) using the FFDM and DDSM datasets together by combining the datasets (mixing up) for both training and testing, and finally (iv) using incremental transfer learning by pretraining the CNN model on the DDSM dataset after pretraining on the ImageNet dataset. Note that in all four experiments the base CNN models are pretrained on the ImageNet and fine-tuned with the FFDM dataset.
To explore clinical caution for reducing the risk of false negatives in recalling a woman, we performed additional classification experiments using CNN models to distinguish false negatives from recalled-benign cases and also from negative cases. Here, we used interval cancers as the false negative cases in the experiments.
The ROC curve (29) was generated and AUC was calculated as a metric of classification accuracy. For the triple classification, since ROC is a binary-class evaluation method, we followed the common practice in literature of generating an ROC curve for each binary-class combination and then reporting the average of the AUCs. Ninety-five percent confidence intervals (CI) were calculated for AUC values using bootstrapping methods (30). DeLong test (31) was used to evaluate the statistical significance when comparing differences of AUCs. All statistical tests were two-sided.
Results on FFDM dataset
As shown in Fig. 1, we found that all categories can be well-distinguished (AUC ranging from 0.66 to 0.81) in the FFDM dataset. The identified imaging features between recalled-benign and negative were most distinguishing (AUC = 0.81; 95% CI, 0.75–0.88). Recalled-benign versus malignant + negative (AUC = 0.76; 95% CI, 0.67–0.86) had the second best performance, followed by malignant versus negative (AUC = 0.75; 95% CI 0.65–0.84). Malignant versus recalled-benign had performance result of AUC = 0.74 (95% CI, 0.65–0.84). Malignant versus negative + recalled-benign had AUC = 0.70 (95% CI, 0.60–0.80). Triple classification (malignant vs. negative vs. recalled-benign) had an average AUC of 0.66.
Performance results for deep learning CNN models for classification on the FFDM dataset. Left, ROC curves for the binary classification scenarios and corresponding AUCs. Right, ROC curves for the triple-class classification scenario and averaged AUC.
Results on DDSM dataset
Figure 2 shows the results on the DDSM dataset. We found AUC ranged from 0.77 to 0.90. Negative versus recalled-benign and malignant versus negative showed the best performances with AUC of 0.96 (95% CI, 0.94–0.98) and 0.93 (95% CI, 0.89–0.97), respectively. Triple classification had an average AUC of 0.85. Recalled-benign versus malignant + negative showed the next best performance at 0.83 (95% CI, 0.77–0.89). Malignant versus recalled-benign and malignant versus negative + recalled-benign showed similar performance with AUCs of 0.78 (95% CI, 0.71–0.85) and 0.77 (95% CI, 0.70–0.84), respectively.
Performance results for deep learning CNN models for classification on the DDSM dataset. Left, ROC curves for the binary classification scenarios and corresponding AUCs. Right, ROC curves for the triple-class classification scenario and averaged AUC.
Results on combined FFDM and DDSM datasets
Figure 3 shows performance results when the two datasets were combined (mixed together) for both training and testing. AUC ranged from 0.76 to 0.91. Best performance was observed for negative versus recalled-benign (AUC = 0.91; 95% CI, 0.89–0.94). With an AUC of 0.84 (95% CI, 0.79–0.88), malignant versus negative had second best performance, followed by malignant versus negative versus recalled-benign with an average AUC of 0.79, then recalled-benign versus malignant + negative (AUC = 0.78; 95% CI, 0.73–0.83). Both malignant versus recalled-benign and malignant versus negative + recalled-benign had an AUC of 0.76 (95% CI, 0.70–0.82). It is observed that when FFDM and DDSM were combined for training and testing, overall classification showed AUC values between FFDM-only and DDSM-only results.
Performance results for deep learning CNN models for classification using combined FFDM and DDSM datasets for training and testing. (Left) ROC curves for the binary classification scenarios and corresponding AUCs. Right, ROC curves for the triple-class classification scenario and averaged AUC.
Incremental transfer learning using the DDSM dataset
DDSM dataset was used to further pretrain the base CNN network (after originally being pretrained on the ImageNet dataset), which was then fine-tuned with the FFDM dataset. Testing data was also from the FFDM dataset. As can be seen in Fig. 4, when compared with results using ImageNet pretrained model, all scenarios showed an increase in performance using this incremental transfer learning strategy, although not statistically significant (P > 0.05). Malignant versus recalled-benign showed the greatest increase in performance increasing from 0.75 to 0.80 (95% CI, 0.71–0.88, P = 0.10) or 5%, followed by malignant versus negative + recalled-benign (AUC = 0.70–0.74; 95% CI, 0.65–0.84; P = 0.15). Malignant versus negative and recalled-benign versus malignant + negative both had their AUC increase by 0.03, 0.74 to 0.77 (95% CI, 0.68–0.87; P = 0.30) and 0.76 to 0.79 (95% CI, 0.70–0.87; P = 0.31) respectively. Negative versus recalled-benign and the triple classification (malignant vs. negative vs. recalled-benign) had the least increase (2% or 0.02 in AUC, 0.66–0.68 for triple classification), although negative versus recalled-benign had the best performance of all the scenarios [AUC = 0.83 (95% CI 0.76–0.87; P = 0.34) compared with 0.81 for ImageNet pretrained model].
Comparison of performance results of deep learning CNN models on different pretraining strategies: using original ImageNet pretrained model versus using model pretrained on ImageNet and DDSM dataset. All the AUCs were results based on training (fine-tuning) and testing on the FFDM dataset.
Robustness analysis
In the robustness analysis using 10% and 15% data for testing (Fig. 5), as expected, we see a slight decrease in the AUC performance in almost all the scenarios compared with those using 5% for testing. This is partly due to the reduced amount of data for training. We also found that the performances overall remained relatively stable, demonstrating the robustness of our CNN models on a varied amount of data for training and testing.
Comparison of performance results using varying amounts (5%, 10%, and 15%) of testing data across all models: FFDM-trained model (top left); DDSM-trained model (top right); FFDM + DDSM–trained model (bottom left); incrementally pretrained CNN models in all scenarios (bottom right).
False negative analysis
The CNN models were further tested on correctly identifying false negative cases/images. In our FFDM dataset, there are 34 interval cancer patients examined as false negative cases. We retrained the CNN models (both the malignant vs. negative scenario and the malignant vs. recalled-benign scenario) on the FFDM dataset only and excluding those interval cancer patients, and then tested these models by inputting the false negative images to the models. Our results showed that 71.3% (by the malignant vs. negative model) and 63.6% (by the malignant vs. recalled-benign model) of the entire false negative images can be correctly identified by our models. Furthermore, when we retrained the CNN model by including 50% of the false negative cases and used the rest of the unseen 50% for testing, we see improved results, as expected, that is, 72.8% (by the malignant vs. negative model) and 68.4% (by the malignant vs. recalled-benign model) of the entire false negative images can be correctly identified. The CNN models trained by other strategies (i.e., experiments iii and iv in Section "Evaluation and statistical analysis") also achieved similar performance (results not shown).
In this study, we present a novel investigation showing that automatic deep learning CNN methods can identify nuanced mammographic imaging features to distinguish negative, recalled-benign, and malignant images. We demonstrated that it is feasible to discover subtle imaging distinction for six classification scenarios on two different imaging datasets.
Among the six scenarios, negative versus recalled-benign showed the best performance. This scenario also had the greatest amount of data, which may have contributed to this improved performance. The distinction in this scenario implies that certain imaging features may result in recalled-benign images to be recalled rather than being determined negative in the first place. In general, the relatively higher AUCs in negative versus recalled-benign and also malignant versus recalled-benign indicate that there are imaging features unique to recalled-benign images that the CNN-based deep learning can identify and potentially use to help radiologists in making better decisions on whether a patient should be recalled or is more likely a false recall. The six classification scenarios were designed to reveal different aspects of performance of the CNN models. It however remains to be determined which scenario, especially in terms of binary-classification or triple-classification, would be the most useful and meaningful choice in implementing a real-world CNN model to help radiologists make recall decisions in a clinical setting. There are noticeable variations in the classification performance between binary and triple classification. This is something to be further investigated in conjunction perhaps with reader studies to evaluate their clinical effects in depth. Of note, with the ability of the CNN model to distinguish between negative and malignant images, the results indicate that deep learning can also perform well in computer-aided diagnosis of breast cancer.
In terms of diagnosis (i.e., malignant vs. negative), literature has reported a great reader variability of radiologists in sensitivity and specificity in screening mammography (4). Using previously reported radiologists' overall sensitivity 86.9% as a reference threshold (4), our best deep learning model for classifying malignant versus negative yields a specificity of 87%, comparable with radiologists' overall specificity of 88.9%. This is encouraging for the current scale of our datasets. With enlarged datasets, we would expect further improvement in our model's performance. In terms of our major motivation of this work for reducing recalls, the lack of exact radiologists' performance data in the literature prevents us from making a reasonable comparison to our CNN models. We aim to further investigate this with a reader study in future work.
Our CNN models demonstrated encouraging results in the false negative analysis. Prevention of false negatives may be an important reason for the high recall rate in current clinical practice. By examining classifications of the interval cancer cases, it indicates a potential of the CNN models to help correctly identify as malignant a majority of false negative cases from recalled-benign cases or from negative cases. This also implies that some cases may be recalled due to certain imaging indications besides the intention of preventing false negatives. Further study into this issue on a larger cohort of false negative cases is of great clinical importance.
In terms of dataset, we started with a digital mammography dataset, FFDM, as it is the current standard screening mammography examination. To demonstrate that our CNN models can be used on a dataset from an independent institution, we tested our models on the DDSM dataset. The DDSM alone showed the best performance overall. This may be due to the larger dataset size or something intrinsic to the characteristics of the DDSM dataset. When the FFDM and DDSM datasets were combined, the overall improved performance was observed in comparison with using FFDM alone. This indicates that our CNN model is robust to an external dataset and its performance can be further improved by including additional data.
Because the DDSM dataset is based on digitized film mammography images and current clinical practice has moved to digital mammography, we wanted to determine the best use of the DDSM to improve results on the FFDM dataset. The most straightforward way was simply to combine it with the FFDM datasets, which improved results when testing on the combined datasets. More importantly, we demonstrated a novel approach of incremental transfer learning using the DDSM dataset, which enhanced the performance consistently on the FFDM dataset in all six scenarios. The incremental transfer learning we used was a two-phase transfer learning approach using two different datasets consecutively (i.e., ImageNet then DDSM). The improvement may be due to using DDSM to fine-tune the model's weights rather than directly in training, allowing the noise from being a different type of mammography dataset to be dampened. It could also be related to the strengths of DDSM as a medical imaging modality that is close to the target modality (i.e., digital mammography) in fine-tuning the weights learned from pretraining on the nonmedical imaging dataset of ImageNet. Many studies have shown the benefit of transfer learning in medical domains with limited data (32). Our results in this study provide deeper insights in developing more optimized transfer learning strategies. However, the incremental transfer learning and the observations made here needs to be evaluated by further analyses and comparative studies in future work.
In comparing the various deep/transfer learning strategies, we show that adding/mixing a larger independent dataset, even if it is not exactly the same imaging modality, helped improve the CNN models in our classification tasks. Furthermore, using this kind of additional dataset in an incremental transfer learning approach has shown a trend of boosted model performance. Although the AUC increases are not statistically significant, additional data will help further evaluate this finding. As to mixing two datasets or using transfer learning, it depends on the specific scenarios and their actual classification performance. Although we have shown encouraging results in this study, we believe this is still an open question meriting further investigation.
In traditional computer-aided detection or diagnosis, the models are usually based on predefined features, which require preemptive determination of which features will work best for the task at hand. In contrast, with the deep learning method we utilized, predefinition of the imaging features is not necessary and are learned automatically from labeled data. Deep learning allows nuanced features to be determined by the learning algorithm for the targeted task, where intrinsic features that may not be identifiable by human visual assessment can be automatically identified and used for imaging interpretation. This study illustrated the encouraging effects of such automatic feature identification by deep learning.
Our study has some limitations. Although we have two independent datasets, additional datasets, especially another digital mammography dataset, can further bolster the evaluation of the deep learning models. We are currently trying to obtain external datasets for such experiments. Also, it is still not clear why the DDSM performed substantially better than the FFDM dataset; this requires further exploration. Of note, digital breast tomosynthesis is increasingly being used in clinical practice, and it has been shown to reduce recall rates (33); thus, incorporation of tomosynthesis data in our study will be important future work. Although we utilized AlextNet CNN structures in this study, comparison with other network structures such as Residual Network (34), VGG (35), or GoogLeNet (36) will be useful to gain further insights on the potential of deep learning.
Finally, it would be useful to present to radiologists the subtle imaging features found by our CNN models in distinguishing the different groups. However, we are not yet able to clearly visualize and clinically interpret what the identified nuanced imaging features are for recalled but benign images or for the other classification categories. At this phase, deep learning is often referred to as a "black-box" due to the lack of interpretability of the identified features. A current area of investigation we are exploring is to visualize the CNN-identified features to be more intuitively perceived by radiologists. The complexity of deep learning network structures, parameters, and data evolving process across different network layers, however, make feature visualization very complicated, requiring in-depth research. Further technical advancement in this active research area is expected to contribute to addressing this important issue.
In summary, we showed that the three different imaging reading categories (malignant, negative, and recalled-benign) could be distinguished using our deep learning–based CNN models. We believe our study holds great potential to incorporate deep learning–based artificial intelligence into clinical workflow of breast cancer screening to improve radiologist interpretation of mammograms, ultimately contributing to reducing false recalls.
J.H. Sumkin reports receiving commercial research grants from GE and Hologic. No potential conflicts of interest were disclosed by the other authors.
Conception and design: J.H. Sumkin, S. Wu
Development of methodology: S.S. Aboutalib, A.A. Mohamed, S. Wu
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S.S. Aboutalib, M.L. Zuley, S. Wu
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S.S. Aboutalib, A.A. Mohamed, S. Wu
Writing, review, and/or revision of the manuscript: S.S. Aboutalib, A.A. Mohamed, W.A. Berg, M.L. Zuley, J.H. Sumkin, S. Wu
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.H. Sumkin, S. Wu
Study supervision: W.A. Berg, S. Wu
This work was supported by NIH/NCI grants (#1R01CA193603, #3R01CA193603-03S1, and #1R01CA218405; to S. Wu, A.A. Mohamed, W.A. Berg, and M.L. Zuley), a National Library of Medicine (T15LM007059) grant (to S.S. Aboutalib), a Radiological Society of North America (RSNA) Research Scholar Grant (#RSCH1530; to S. Wu), a University of Pittsburgh Cancer Institute Precision Medicine Pilot Award from The Pittsburgh Foundation (#MR2014-77613; to S. Wu, A.A. Mohamed), and a University of Pittsburgh Physicians (UPP) Academic Foundation Award (to S. Wu). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.
Revision received June 19, 2018.
Accepted July 31, 2018.
Published first October 11, 2018.
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You are going to email the following Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening
Clin Cancer Res December 1 2018 (24) (23) 5902-5909; DOI: 10.1158/1078-0432.CCR-18-1115
Radiation Depletes Skin Malignant T Cells in CTCL
IDH Mutations and Genomic Profiling in Chondrosarcoma
Plasma cfDNA in Glioblastoma
Show more Precision Medicine and Imaging


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Tag Archives: Sports
July 19, 2019 By adminatcci
After Whitewater Canoeing, Kerns Finds Grad School Smooth Sailing
Journalism PhD student Charli Kerns has fought her way through whitewater rapids and cascaded over waterfalls in her canoe. Knowing that adrenaline rush, she doesn't get too stressed out over research, writing, or teaching a class.
Kerns been an action sports enthusiast since being introduced to kayaking through UT's Outdoor Pursuits during her undergraduate days on Rocky Top. She later picked up canoeing and now considers whitewater canoeing her main sport. She's traversed waterways across the United States as well as in Canada, Costa Rica, Mexico, Germany, France, and India.
She's managed to translate her love for action sports—the preferred … Read more "After Whitewater Canoeing, Kerns Finds Grad School Smooth Sailing"
January 29, 2018 By adminatcci
Recent JEM grads receive Emmys for their work in news, sports reporting
JEM is excited to congratulate Annie Carr and Dallas Abel on their Emmy wins!
Annie Carr's work on WBIR newscasts covering the Sevier County wildfire won her a Regional Emmy Award (Midsouth) for top evening newscast. Annie currently produces special projects and the 6 p.m. newscast at WBIR. This is Annie's second nomination and first win. Annie started working at WBIR when she was still a student. She was also involved in a number of organizations at UT including: TVC, the Medal of Honor Project and UT Today.
Dallas received a Regional Emmy Award (Chicago/Midwest) for outstanding achievement … Read more "Recent JEM grads receive Emmys for their work in news, sports reporting"
Annie Carr
Dallas Abel
Emmy Award
JEM alumni
UT CCI Grad Brian Canever Wants to Change the World with Sports
The University of Tennessee Center for Sport, Peace, and Society is an organization that works to promote empowerment and peace building through sports. Brian Canever is the digital content manager for the center and holds a master's degree in communications and information with a concentration in journalism and electronic media from the University of Tennessee, Knoxville (UTK). Brian has interned at ESPN and written for publications like Bleacher Report and the Knoxville News Sentinel, but he has fully embraced his role at the center. We had the opportunity to speak to Brian about what exactly he does in his … Read more "UT CCI Grad Brian Canever Wants to Change the World with Sports"
Brian Canever
October 26, 2012 By adminatcci
Alumnus Spotlight: Zac Ellis takes over Heisman Watch column on SI.com
JEM graduate Zac Ellis has been named the new author of the Sports Illustrated Heisman Watch column on SI.com.
"It's called Heisman Watch, which is exactly what it sounds like: I rank the field's Heisman contenders based on each week's performances," Ellis said in an e-mail to Dr. Elizabeth Hendrickson about the column.
In addition to writing the column, which will appear every Monday, Ellis will have the opportunity to travel to the Heisman Award ceremony in New York City in December and will be asked to submit his vote for the award.
Ellis graduated from the University of Tennessee … Read more "Alumnus Spotlight: Zac Ellis takes over Heisman Watch column on SI.com"
Zac Ellis
March 9, 2012 By adminatcci
JEM Sports Journalism graduate makes top 5 in Xfinity's Sports Social Media Star contest
"It was the experience of a lifetime and I am so privledged to have gotten this opportunity."
Lexy Jochim graduated with a degree in Sports Journalism in 2005 and returned yesterday from Xfinity's Sports Social Media Star contest in Phoenix. Jochim, along with hundreds of other candidates, campaigned on social media sites Twitter and Facebook to gain votes and she was told last week that her hard work had paid off — she was chosen for the top 5. Jochim told us,
"It was a whirlwind weekend. I got to go behind the scenes of the White Sox Training Facility … Read more "JEM Sports Journalism graduate makes top 5 in Xfinity's Sports Social Media Star contest"
JEM Student Awarded 1st Place in SPJ Mark of Excellence Competition
JEM Senior Zac Ellis placed first in the Society of Professional Journalists' Region 12 Mark of Excellence Awards in the category of Sports Writing.
Ellis currently serves as the Editor in Chief for the The Daily Beacon, and previously interned with Sports Illustrated in Summer 2010 through an internship from the American Society of Magazine Editors.
You can find a complete list of the SPJ Mark of Excellence Award winners here.
JEM Students

redpajama_set_name: RedPajamaCommonCrawl


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Michael Jackson's White Glove Is Up for Auction
In this July 7, 1984 file photo, Michael Jackson wears a white glove during his performance kicking off the "Victory Tour" at Arrowhead Stadium in Kansas City, Mo.
Cliff Schiappa—AP
By Ariana Bacle / Entertainment Weekly
One of Michael Jackson's trademark single white gloves is currently up for auction — but fans will have to shell out at least $20,000 to get their hands on it (or rather, in it).
Nate D. Sanders' auction house is handling the sale, which ends July 30 at 5 p.m. PT. Jackson gave the glove, which features crystal beading and light wear and tear, to personal artist Paul Bedard — who worked on multiple pieces of art for Jackson's Neverland Ranch — in 1984, and Bedard went on to sell it in 2005.
Head to Sanders' site before July 30 to bid and see photos of the item.
This article originally appeared on EW.com
See Unpublished Photos of Michael Jackson From the 70s
A young Michael Jackson sits in a hotel room in New York City in the 1970s.
Alix Dejean
Joe Jackson in New York City in the 1970s.
Michael Jackson and Tito Jackson in New York City in the 1970s.
Rebbie Jackson prepares to go on stage in New York City in the 1970s.
Rebbie Jackson at a party in New York City in the 1970s.
Michael Jackson performs in New York City in the 1970s.
Michael Jackson and the Jackson 5 perform in New York City in the 1970s.
Janet Jackson and Randy Jackson perform in New York City in the 1970s.
Michael Jackson poses for a photograph in New York City in the 1970s.
Michael Jackson sits alone in a movie theater in New York City in the 1970s.
Michael Jackson and Mamadou Johnny Sekka in New York City in the 1970s.
From left: Tito Jackson, Randy Jackson, Michael Jackson, and Mamadou Johnny Sekka in New York City in the 1970s.
Randy Jackson in a hotel room in New York City in the 1970s.
From left: Michael Jackson, Sandra Trim-DaCosta, Jackie Jackson and Tito Jackson in New York City in the 1970s.
Randy Jackson in New York City in the 1970s.


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Hyperfish Welcomes Jeremy Thake as VP of Product Technology
Published on: November 20, 2016
KIRKLAND, Wash., Nov. 7, 2016 /PRNewswire/—Hyperfish today announced Jeremy Thake will be joining their expanding team as VP of Product Technology. Working alongside co-founders Brian Cook, Chris Johnson, as well as VP Sales Jakob Maciolek, Jeremy will head up the evangelism at Hyperfish, while supporting and enabling customers and partners alike in their success with Hyperfish the service. Hyperfish allows organizations to obtain the full value of Office 365 through complete and relevant profiles.
"Jeremy is a highly respected, knowledgeable and trusted member of the Microsoft community and we are excited to bring his skills and talent to Hyperfish," said co-founder and CTO Chris Johnson. "Jeremy's intricate knowledge of SharePoint, Office 365, and Azure will support our delivery of solutions that meet the current and future needs of companies seeking the greatest return from their Microsoft investment."
Jeremy has established a longstanding career in the Microsoft ecosystem, most notably in SharePoint and Office 365. Jeremy's affiliation with SharePoint began in Australia as a Consultant and Architect on the earliest versions of SharePoint. As of 2010 Jeremy transitioned into Product Marketing and Evangelism roles focused on the promotion of the Office and Azure developer services.
Jeremy joins Hyperfish following 3 years at Microsoft, most recently in the role of Senior Product Marketing Manager—Azure App Service, where he was responsible for the positioning, messaging, and raising global awareness and usage of Azure. Immediately prior to Microsoft, Jeremy held the position of VP of Global Product Innovation at AvePoint, helping bring to market the latest technology for the migration, management, and protection of Office 365 and SharePoint data.
Hyperfish recently launched at Microsoft Ignite their first product offering, a SaaS solution enabling organizations to quickly and easily identify, retrieve, and populate missing information in Office 365 and Active Directory (AD).
"I've worked in the Office 365/SharePoint space for a long time. Missing information has been an issue for every customer I've worked with except Contoso and Adventure Works. I'm really excited to work at Hyperfish and make every company as productive as those two," said Thake.
Elyse is Marketing Manager at Hyperfish. She has over 10 years of marketing and operations experience in the technology sector. An avid traveler, Elyse has lived and worked across three continents.
News SharePoint


A carrot is one of the Cropsie types in Farm Heroes Saga.
It has got two very big eyes.
Carrots are first needed in level 3, the third level in Dairy District.
Rob is the blocker which is stealing carrots. It can make carrots harder to collect on certain levels.
If the player plays this game via King.com, he/she may choose it as his/her avatar.
It also appears on Farm Heroes Super Saga along with water drop.


maybe but these might be easier to complete than with just en's population, last couple card campaigns i couldn't finish.
well this one will run till February 21. Hope cben will release info on account migrating before that. Then decide whether to migrate or start anew in jp server.
What kind of performance we are talking about actually? I don't think there is any difference. Game clients are same, difference is obviously in game server and managemen.
I just heard that the game runs better / faster with the JP client. Wasn't expecting it to be true anyway since I remember it being the same.


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Current Affairs for UPSC IAS Exam – 19 April 2020
Home » Current Affairs for UPSC IAS Exam – 19 April 2020
CA April 2020
Govt. nod mandatory for FDI from neighbouring countries
Most online content on child sexual abuse from India
RT-PCR only confirmatory test, says government
Hydroxychloroquine does not reduce viral load
The COVID-19 virus and its polyproteins
CII seeks amends to govt. norms
Focus: GS-III Indian Economy, GS-II International Relations
In a move that will restrict Chinese investments, the Centre has made prior government approval mandatory for foreign direct investments from countries which share a land border with India. Previously, only investments from Pakistan and Bangladesh faced such restrictions.
The revised FDI policy is aimed at "curbing opportunistic takeovers/acquisitions of Indian companies due to the current COVID-19 pandemic," said a press release from the Department for Promotion of Industry and Internal Trade on 18th April 2020.
What does New Policy say?
A non-resident entity can invest in India, subject to the FDI Policy except in those sectors/activities which are prohibited.
However, an entity of a country, which shares land border with India or where the beneficial owner of an investment into India is situated in or is a citizen of any such country, can invest only under the Government route.
Pakistani investors face further restrictions in requiring government approval for FDI in defence, space and atomic energy sectors as well.
Investors from countries not covered by the new policy only have to inform the RBI after a transaction rather than asking for prior permission from the relevant government department.
The official statement added that a transfer of ownership of any existing or future FDI in an Indian entity to those in the restricted countries would also need government approval.
The decisions will become effective from the date of the Foreign Exchange Management Act notification.
Why was this necessary?
India shares land borders with Pakistan, Afghanistan, China, Nepal, Bhutan, Bangladesh and Myanmar.
With many Indian businesses coming to a halt due to the lockdown imposed to contain the COVID-19 pandemic and valuations plummeting, a number of domestic firms may be vulnerable to "opportunistic takeovers or acquisitions" from foreign players.
In the third week of April 2020, housing finance company HDFC informed the stock exchanges that the People's Bank of China now holds a 1.01% stake in the company. This was an instance of portfolio investment through the stock market and not FDI.
Given the macro situation, it is a measure to protect vulnerable companies, with possibly low valuations, from unwelcome takeovers.
Focus on China
China's footprint in the Indian business space has been expanding rapidly, especially since 2014.
The total current and planned Chinese investment in India has now crossed $26 billion, according to estimates in the March 2020. However, major Chinese investments in India span a range of sectors.
This blurred separation between state-owned enterprise and private enterprise raises the question of whether the Chinese private sector can indeed be considered as an entirely distinct entity from the state.
This question becomes even more relevant with Chinese and other foreign firms acquiring controlling stakes in Indian companies, particularly in the technology sector where definitions of security or strategic implications are rapidly evolving
Criticism of the move: Unintended Problems
The move is in reference to the possibility of Chinese investors purchasing undervalued shares of Indian-listed companies as on April 12, news of an incremental purchase of shares in HDFC made by the People's Bank of China made the headlines.
However, the notification does not restrict its application to such cases.
A plain reading of the amended policy makes every type of investment by Chinese investors subject to government approval.
It neither distinguishes between greenfield and brownfield investments nor listed and unlisted companies.
It also does not distinguish between the different types of investors, such as industry players, financial institutions, or venture capital funds.
Such a blanket application could create unintended problems.
For instance, it is likely that unlisted or private companies might find themselves under financial stress due to the COVID-19 pandemic.
For instance, it is likely that unlisted or private companies might find themselves under financial stress due to the COVID-19 pandemic. The absence of a white knight may cause bankruptcy and job losses.
Greenfield investments are another category where the new rules may pose obstacles. These are investments where Chinese investors bring fresh capital to establish new factories and generate employment in India.
Moreover, the most visible 'Chinese investors' in India, most in the Internet space, may not even come under the definitions of the new rules.
It will prove to be extremely difficult to attribute nationality to venture capital funds or fix the ultimate beneficial ownership of listed companies down to founders of a certain nationality.
By abolishing the Foreign Investment Promotion Board in 2017, India took the decision of dismantling the last vestige of an FDI regime that sought to block sensitive foreign investments.
Focus: GS-II Social Justice
In a global compilation of reports of child sexual abuse material (CSAM) found online, India stands right on top of the list, with 11.7% of the total reports or at 19.87 lakh reports, followed by Pakistan, which contributes 6.8% of all reports (11.5 lakh reports) and Bangladesh comes in fourth with 5.5 lakh reports and a share of 3.3%.
Gravity of the Situation
In 2019, the Centre received a total of 1.68 crore reports. The material thus reported by the members of the public and electronic service providers, principally comprises still pictures and videos depicting children in a clear sexual angle.
Three of the top four countries were in South Asia, raising concerns among child rights activists about the online safety of children in the region.
The National Centre for Missing and Exploited Children (NCMEC) adds that the reports include geographic indicators related to the upload location of the child sexual abuse material, but country specific numbers may be impacted by proxies and anonymisers.
There is also concern that the lockdowns across the world will lead to an exacerbation of the situation.
The NCMEC urges people to report CSAM found online across the world annually, on their online platform CyberTipline.
National Centre for Missing and Exploited Children (NCMEC)
The National Center for Missing & Exploited Children (NCMEC) is a private, non-profit organization established in 1984 by the United States Congress.
NCMEC handles cases of missing or exploited children from infancy to young adults through age 20.
The NCMEC operates the CyberTipline which was established by Congress to process reports of child sexual exploitation (including child pornography, online enticement, and contact offenses).
The NCMEC reviews these reports and shares them with the appropriate law enforcement agency or Internet Crimes Against Children (ICAC) task force
Child Abuse in India
While we have successfully brought in children-specific legislations such as the Prevention of Children from Sexual Offences Act 2012 (POCSO) and the amended Juvenile Justice (Care and Protection of Children) Act 2015, our performance in creating robust and reliable preventive response systems has been markedly poor.
The spate of brutal crimes against children demonstrates that our collective approach to child safety in schools remains ad hoc, laissez-faire and poorly monitored, highlighting the lack of both soft and hard preventive infrastructure.
Every six minutes, a child goes missing. More than 4.5 lakh children have been victims of trafficking, bought, sold for commercial and sexual exploitation. Over 50% children in India were subjected to one or another form of physical abuse. In 94.8 percent of cases, children were raped by someone known to them, according to data collected by the National Crime Records Bureau.
Convention on Rights of Child, 1989
According to the convention, any child below the age of 18 is called a child. Around 140 countries have signed the convention.
India is also a signatory. The convention sets out social, political, economic, civil and cultural rights of every child.
The rights include right to education, right to protection against physical abuse, right to rest and leisure.
Why do the perpetrators choose children as targets?
Many child abuse cases go unreported because the victim is traumatised and therefore reluctant to confide in anyone.
In a majority of the cases, the family of the victim may not want to confront the abuser if he or she is an influential family member or an influential person in society, for the fear of backlash or shame.
Focus: GS-III Science and Technology
The Union Health Ministry has said that the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard frontline test for coronavirus (COVID-19) and that rapid antibody test cannot replace it.
The statement comes after the Indian Council of Medical Research (ICMR)'s national taskforce issued guidelines on testing strategy to all States, following a review of the worldwide testing methodology.
What is the use of Rapid Antibody Test?
The Rapid Antibody Test gives us an idea about prevalence of disease in a particular area and thus is used for epidemiological studies and surveillance purpose in hotspots.
It can also be used in districts which are not hotspots to study emerging trends.
Data generated by surveillance through rapid testing can be used for contact tracing if any positive case arise in an area.
When to conduct RT-PCR and Rapid Antibody Test?
Presently the government is using the RT-PCR tests to detect novel coronavirus from samples of throat or nasal swabs of people with symptoms or high-risk individuals who might have come in contact with positive patients.
Testing Strategy has been spelt out for when RT-PCR or Rapid Antibody Tests are to be done.
In hotspot areas, for persons having had fever, cough or cold for less than 7 days, RT-PCR test is to be done and then on basis of result, positive cases are to be given treatment and negative cases are to be taken as susceptible.
In case of having had symptoms for more than seven days, Rapid Antibody Test is to be done and then if result is positive, person is to be kept in quarantine and contact tracing is to be done, and for negative results, persons are to be monitored for 14 days and if there is any clinical suspicion, then the case is to be monitored at clinical level and PCR test can be done if required.
What is RT-PCR?
Reverse transcription polymerase chain reaction (RT-PCR) is a laboratory technique combining reverse transcription of RNA into DNA (in this context called complementary DNA or cDNA) and amplification of specific DNA targets using polymerase chain reaction (PCR).
It is primarily used to measure the amount of a specific RNA. This is achieved by monitoring the amplification reaction using fluorescence, a technique called real-time PCR or quantitative PCR (qPCR).
Combined RT-PCR and qPCR are routinely used for analysis of gene expression and quantification of viral RNA in research and clinical settings.
Chitra GeneLAMP-N
Results of a randomised, control trial using hydroxychloroquine drug in 150 COVID-19 patients show that the drug does not reduce the viral load even on day 28.
Details of the trial and its findings
This is the first trail using hydroxychloroquine where patients have been randomised to receive either the drug or just standard of care.
However, patients who got the drug did show quicker alleviation of clinical symptoms, possibly through anti-inflammatory properties and improvement in white blood cell count.
The drug did not cause any serious adverse effects in patients. But some patients did experience adverse effects.
The most common adverse event in patients who got the drug was diarrhoea (10%).
How significant are the results?
These encouraging results suggest clinical benefits of adding hydroxychloroquine into the current standard management to limit inflammatory response, which is the key to prevent systemic inflammation and subsequent multiple organ failure and death.
The shorter time to alleviate the symptoms might be useful for preventing disease progression.
What is hydroxychloroquine?
Hydroxychloroquine (HCQ) is an anti-malarial drug similar to chloroquine, one of the oldest and best-known anti-malarial drugs, but with lesser side-effects.
It can be bought over the counter and is fairly inexpensive.
The Indian Council for Medical Research (ICMR) has cleared HCQ to be used as a prophylaxis, or preventive medication, by doctors, nurses and other health staff who are involved in treatment and containment of COVID-19.
What is the difference between a virus and a bacterium?
Bacteria are alive. Each bacterial cell has its own machinery to reproduce itself.
Take a bacterial cell, and put it in a solution containing nutrients, it grows itself and multiplies in millions. The genes in the cells (genome, made up of DNA molecules, the information contained in which is transcribed as a message to the messenger molecules called RNA), and the message therein is translated into action molecules called proteins, which are the foot-soldiers that help the growth and multiplication of the bacterium.
Coronaviruses do not have DNA as their genome, but RNA; in other words, they can only translate and not transcribe.
Thus, they are 'dead', unable to renew and grow themselves; they need help. This they achieve by infecting 'host cells' which they bind to, and multiply by the millions.
With no host cell to help, a virus is simply a dead storage box.
The polyprotein strategy
Upon infection, the entire RNA with its 33,000 bases is translated in one shot as a long tape of amino acid sequences. Since this long chain contains several proteins within it, it is called a "polyprotein" sequence.
One needs to analyse this long chain, find the relevant proteins, isolate and study what each of them does in helping infection.
This strategy allows the viral genome to be compact, and express the protein when the need arises.
How do drugs work and where India stands on developing a drug?
We thus have a large set of proteins in the virus, against which a number of potential molecules and drugs can be tried to interfere and stop the production of these viral proteins.
India is well versed with expertise in the area of organic and medicinal chemistry since the last 90 years and in manufacturing quality drug molecules, and exporting them for use at home and across the world since the 1970 patents act of India.
Our expertise today, in both the public and private sector, includes not just synthesizing made-to-order molecules, but has added new methods involving computer modelling of target proteins from bacteria and viruses, homology modelling, drug design, repurposing of drugs, and other methods.
The Council of Scientific & Industrial Research (CSIR) has taken upon itself the express task of coming out with molecules and methods to counter the dreaded virus.
Focus: GS-III Industry and Infrastructure, Disaster Management
The Confederation of Indian Industry (CII) has called for certain modifications in the guidelines issued by the government on resumption of economic activities from April 20, to avoid disruptions in the supply chain.
CII Suggestions
CII has suggested continued stringent lockdown in specified containment zones with the rest of the identified hotspot districts to be open to economic activities with adequate safety measures.
The industry body has suggested that red zone districts and containment zones need to be clearly identified and demarcated and industrial activities be permitted in non-containment zones of red zone districts, if found safe.
A list of red districts and containment zones may be published on a real-time basis for information of industry.
Automotive value chain, including OEMs, components, retail and service workshops, must also be included in the list of permitted industries.
Agricultural inputs of fertilizers, pesticides and seeds have been opened up, and there is a need to also relax rules for production of necessary equipment.
Similarly, equipment and services for generation, transmission and distribution of power, including renewable energy such as gear boxes and generators may be allowed to function.


Japanese Episode DP 022: "Pachirisu vs. Aipom! Contest Battle!!"
American Episode 486: "Borrowing On Bad Faith"
Satoshi is training for his upcoming gym battle one day when his Aipom takes his hat and runs off with it! The monkey pokemon is too fast for Satoshi and loses its trainer before ending up stranded on a rocky cliff! Musashi-tachi, who had just happened to be passing by, notice the Aipom in peril and decide to help it. After saving the pokemon's life, Musashi learns that the pokemon belongs to Satoshi! The young trainer thanks the Rocket-Dan member, but Musashi declares that his gratitude isn't enough! She demands to be given the opportunity to compete with Aipom in an unofficial Pokemon Contest being held in a nearby village, so Satoshi reluctantly agrees. Satoshi keeps a watchful eye on Musashi and Aipom during their training but finds nothing suspicious. The Contest begins, and Hikari is the first Coordinator up. Her Pachirisu, after getting over a bit of stage fright, accidentally electrocutes the entire audience with a Discharge attack! Hikari thinks her chances of getting into the next round are slim to none, but luckily for her, the rest of the competition isn't so great. After Musashi and Aipom dazzle the crowd with a combination of Double Team and Swift attacks, it is declared that both Hikari and Musashi will move on to the second round. As the second round goes underway, Satoshi becomes worried that his Aipom might like Musashi better than it likes him. Musashi and Aipom eventually win the battle and claim the prize of a year's worth of bananas! As the Rocket trio make their exit, Aipom starts to chase after them, confirming Satoshi's worst fears! However, it is soon learned that Aipom only helped Musashi out to get its paws on the bananas! After sending the Rocket-Dan blasting off again, a relieved Satoshi rejoins his Aipom on the road to Sono'o Town.
This episode reminds me a lot of that "Pikachu's Entering the Rocket-Dan!?" episode from Advanced Generation. Only this time, instead of the clichéd amnesia bit, we get a legitimate reason for Aipom to join up with the Rocket-Dan. It also helps the whole "Aipom really likes Contests" thing the anime's been doing for a little while now, instantly making this episode more valuable to the series than that Houen filler was.
We also get to see Musashi win a Contest for the first time, but the fact that it's an unofficial Contest and she doesn't even get to keep the prize she earned is pretty lame. Is it really too much to ask to give the Rocket-Dan a happy ending every now and then?
For some reason, the dubbed version refuses to call the bananas by their name, instead referring to them as fruit. When I noticed this, I thought PUSA was going to make some DP 007-esque joke, but they didn't go that route. Weird.
PUSA keeps an astounding (for them) eight pieces of Japanese music, so that's worth noting as well.
The last shot of the episode is a few seconds longer in the dub as it is in the Japanese version.


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Q: What assemblies can I remove when removing Entity Framework from a .net project? I have a cs webproject that used to use MS Entity Framwork (v4 or 5 I think) for data access. All data access code is now moved to LLBLGEN.
Which of the following references can I safely remove:
System
System.Core
System.Data
System.Data.DataSetExtensions
System.Data.Entity
System.Drawing
System.EnterpriseServices
System.Runtime.Serialization
System.Security
System.Transactions
System.Web
System.Web.ApplicationServices
System.Web.DynamicData
System.Web.Entity
System.Web.Extensions
System.Web.Services
System.Xml
System.Xml.Linq

A: It's hard to say for sure because we don't know what the rest of your project is using. At a minimum, you should be able to remove System.Data.Entity. The DataSetExtensions are still in the default list, but primarily apply to LINQ to Datasets which I don't think you'll be using so you should be safe to pull that. It's hard to say if you need the rest or not as we don't know your project.

redpajama_set_name: RedPajamaStackExchange


Avon Fire & Rescue Service's (AF&RS) Chew Magna Fire Station is now protected against flooding as a result of a new partnership agreement with UK Flood Barriers.
While UK Flood Barriers was carrying out flood defence work in the village of Chew Magna, AF&RS allowed the company to use the facilities at the fire station to store their equipment. In return UK Flood Barriers agreed to fit flood defences and provide rubber bungs for the toilets to stop water getting into the fire station.
Station Manager Martin Collins said: "This agreement between AF&RS and UK Flood Barriers was beneficial to both organisations. We know that the area around Chew Magna is prone to flooding and have seen it occur in recent years.
"The new flood defences mean that in the event of torrential downpours and large amounts of standing water we can erect the barriers and inflate the rubber bungs to prevent water getting into the fire station. We are often called upon to assist the local community during outbreaks of flooding and these defences mean we can continue to operate and assist those who may need our help.
Bath & North East Somerset Council, together with the Environment Agency, has been carrying out property level flood protection work in the village of Chew Magna.
In 2012 retained duty system firefighters camped out at Chew Magna Fire Station to prevent it flooding and to ensure they could respond to calls quickly during a period of heavy rain and flooding. They had to pump and mop out the station three times.


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Why CFOs Need To Cultivate Strong Relationships With The C-Suite
Kyle Lim
Digital technologies have been transforming the way business is done for some time, but the COVID-19 pandemic led to increased adoption of technology amongst business across all industries.
In fact, according to a survey, 77% of CEOs reported that the COVID-19 pandemic led directly to an acceleration of digital transformation plans in their businesses.
This has led to sweeping changes in how successful businesses operate these days, and this include the C-suite executives in these businesses.
As a CFO, being good with numbers and business strategy is no longer enough if you want to be effective in your role. You must also be able to leverage new technologies in order to automate and streamline financial reporting processes, as well as data analysis.
This would enable you to obtain valuable insights from your business's financial data, and put you in a position to advise your fellow C-suite executives on business strategy.
However, to truly reap the full benefits of digital transformation, every executive in the C-suite must individually cultivate technological savvy as well. And as the CFO, you can play a central role in encouraging this by cultivating trusting and productive relationships with your fellow executives.
Here's why it's crucial that you, as the CFO, lead the way for your fellow C-suite executives in adopting digital transformation for your businesses, and reaping the benefits in your respective positions.
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Why It's Important To Cultivate Technological Savvy Among The C-Suite
According to a survey conducted by McKinsey involving 1,331 C-suite executives in a range of industries and company sizes, businesses which go with bolder digital transformation strategies see greater economic success from the effort, than those pursuing digital strategies that involve only incremental changes.
These top-performing companies report an average return of 50% of the full revenue benefits that their digital transformation strategies could have achieved, in comparison with an average of 31% among all respondents in the survey.
According to McKinsey, this is because the top-performers are more likely to adopt practices that enable faster innovation, as well as automated processes for testing and deploying new technology. They also spend twice as much of their digital and technology budgets on building new digital businesses, compared to their less successful peers.
As such, it's clear that to reap as much benefit as possible from their digital transformation efforts, a business needs a C-suite that's technologically savvy as a whole, able to appreciate and utilise the benefits technology brings to their respective roles.
For example, to be effective in their roles today, chief marketing officers (CMOs) must be able to shift away from traditional mass marketing strategies, and adapt targeted and data-driven marketing.
Chief human resources officers (CHROs) could also leverage technology in their roles by implementing online HR systems that facilitate employee self-service capabilities, and leveraging data in their talent management strategy through HR data analytics systems.
From this, we can see that technological savvy amongst the C-Suite is key if a business is to realise the full potential of digital transformation. And as the CFO of your business, it falls to you to cultivate that savvy amongst your fellow C-suite executives.
But how can you do this? By cultivating strong internal relationships between your fellow C-suite executives, and positioning yourself as a leader who can garner support behind any tough business decisions that has to be made.
This is possible because, in your role, you as the CFO are the gatekeeper of the data needed for strategic business planning and analysis. This means you have a central role in conducting the cost-benefit analysis for any investments your business makes in new technology, and in identifying growth areas and opportunities that your business can capitalise on with a digital transformation effort.
But you can't do it alone, which is why building strong relationships with your fellow C-suite executives is so important. You'll need their support and backing if you wish to convince them of the necessity for pursuing a digital transformation strategy, especially if your colleagues are already technologically savvy enough to see the potential benefits from such a course of action.
Building a good working relationship with your CEO is especially important in this regard, for you'll need to rely on your CEO's ability to articulate the case for change across the whole business, communicate a forward-looking digital transformation strategy to all stakeholders, and obtain buy-in from them at all levels within the business.
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Why CFOs Should Build A Strong Relationship With Their CIOs
When it comes to driving digital transformation within your businesses, one of the most important fellow C-suite executive you can build a relationship with is the chief information officer (CIO).
That might go without saying, seeing as how CIOs have always been the resident experts in the opportunities and limitations that new technologies bring the table. But these days, in the wake of the COVID-19 pandemic, they're also seen as the natural leader to drive digital transformation efforts throughout the entire business.
With businesses increasingly moving to a hybrid work model in the aftermath of the pandemic, corporate spending on public cloud services to enable employees to work from home (WFH) is expected to rise to US$495 billion, according to Gartner.
This, plus inflation and rising costs are putting pressure on businesses' profit margins, which means it's even more important for CFOs and CIOs to work together to ensure their IT and tech investments are paying off.
Because of this, when it comes to driving digital transformation within your business, it's especially important that CFOs like yourself cultivate a good working relationship with your CIOs.
In fact, it's proven that businesses where CFOs and CIOs foster good working relationships tend to achieve better outcomes on digital transformation initiatives. That's because businesses with strong CFO-CIO partnerships are 51% more likely to find funding for them, 39% more likely to keep spending on these initiatives within budget, and 18% more likely to achieve the desired goals of such initiatives.
But despite the proven effectiveness of the CFO-CIO partnership, it's not easy to establish one in the first place. According to a Gartner survey of 183 executives, only 30% of CFOs and CIOs report a 'collegial' and 'business-centric' CFO-CIO partnership in their businesses.
This may stem from a disparity in how one side views their level of familiarity in the other side's field of expertise. 80% of CFOs in the survey say that they understand how financial management needs to adapt to foster digitalisation; a statement which only 55% of CIOs agreed with.
Conversely, 94% of CIOs said they grasped how technology affects corporate financials, but only 62% of CFOs agreed with that statement.
Therefore, CFOs like yourself should build closer working relationships with the CIOs in your businesses, especially when it comes to leading digital transformation initiatives. Working together, such a partnership can ask some of the most important questions;
Are investments in IT and tech paying off for the business?
Are these investments providing the return-on-investment (ROI) that was predicted?
Do the outcomes of these investments meet expectations?
To ensure that your business maintains its agility during future disruptive events like the pandemic, and to emerge from such events stronger than your competition, CFOs like yourself must continue maintaining strong partnerships with your CIOs to ensure that digital transformation efforts continue to bear fruit for your business.
Building Strong Relationships With Your CIOs And The Rest Of The C-suite Is Conducive To Digital Transformation
If your fellow C-suite executives in your business are technologically savvy and know how to leverage technology to their benefit in their respective roles, they're much more likely to provide buy-in for any digital transformation initiatives you might propose in your capacity as the CFO.
Because of this, it's crucial that you make an effort to cultivate strong working relationships with your fellow C-suite executives, as advocating for digital transformation initiatives for your business becomes much easier if they're better equipped to understand how it could benefit them.
It's especially important that you build a strong working relationship with your CIO, as they're the most valuable ally you can have in your corner when it comes to bring digital transformation initiatives to fruition.
With a strong CFO-CIO partnership, your business can derive the full potential of digital transformation, and be better positioned to weather future disruptive events on the same scale as the COVID-19 pandemic.
However, to successfully cultivate strong working relationships with the rest of the C-suite, you have to first start with the CEO. That's because the partnership between the CEO and the CFO is usually considered the most critical to the success of your business.
Click on the image below to access Oracle NetSuite's resource containing insights from Rockstar CFO's Jack McCullough on the secrets to a successful CEO/CFO relationship below.
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The LikeTelevision™ Movie channel proudly presents - Thursday's Game - with Gene Wilder, Bob Newhart, Valerie Harper, Ellen Burstyn, Cloris Leachman, Nancy Walker, Chris Sarandon, Rob Reiner, Norman Fell, Richard Gautier and Martha Scott. Harry and Marvin decide to blow off the usual Thursday poker game, and things get crazy when their wives find out. What have they been doing on Thursday night? hilarious comedy with some of the greatest actors of the glory days of television sitcoms.
A classic buddy movie - with some really great scenes, especially Cloris Leachman freaking out in the restaurant, the poker game fiasco, Norman Fell firing Gene Wilder, and of course - Nancy Walker, best known as Madge the manicurist from Palmolive dish washing soap fame - dishwashing liquid? - you're soaking in it! The scene with Rob Reiner is also pretty funny, and Gene Wilder and Bob Newhart are always terrific.
Harry Evers (Gene Wilder) has a terrific scene in a taxi and at the unemployment office. He's worried his job is in jeopardy. We also meet Harry's poker buddy, Marvin Ellison (Bob Newhart). The poker group decides to raise the stakes - what was once a friendly game - gets a little tense as Harry and Marvin win thousands of dollars. Then Bob decides he's not going to pay. After a fight breaks out, it's clear -Thursday's Game is coming to an end. Harry and Marvin have their first night out.
Harry decides to visit a job placement office - even though he still has a job. Some hilarious scenes in the waiting room, plus we meet his job placement headhunter - the great Nancy Walker.
with Cloris Leachman - make sure you check out an old One Step Beyond episode - Dark Room, featuring a very young Cloris Leachman.
Harry is troubled as the ratings continue to slip for his show. He visits his son David at camp - a very weird "touchy-feelie" new age camp - where everyone gets in touch with their feelings. Meanwhile, Harry loses his job as the ratings fall further.
Starring - Gene Wilder, Bob Newhart, Valerie Harper, Ellen Burstyn, Rob Reiner, Norman Fell, Nancy Walker, Chris Sarandon, and Cloris Leachman!
Bob Newhart as Harry's best friend Marvin, the unveiled zipper king!
Marvin strikes it rich with a crazy new fashion design design for uncovered zippers and the money rolls in.
Things start getting tough at home as Harry and Lynn argue and grow distant. She's planning to leave him when she decides to give it another go. Classic scene with Cloris Leachman getting dumped at the restaurant.
Rob Reiner, pre-Spinal Tap, is great too.
Nancy Walker, best known as Rosie the waitress in the Bounty Paper Towel commercials - Bounty, the quicker picker upper!