Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups

Purpose Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. Methods We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. Results In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Conclusion Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.

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Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Journal of Medical Internet Research ◽

10.2196/22624 ◽

2020 ◽

Vol 22 (10) ◽

pp. e22624 ◽

Cited By ~ 1

Author(s):

Ranganathan Chandrasekaran ◽

Vikalp Mehta ◽

Tejali Valkunde ◽

Evangelos Moustakas

Keyword(s):

Health Care ◽

English Language ◽

Latent Dirichlet Allocation ◽

Data Sets ◽

Data Set ◽

Time Period ◽

Combining Data ◽

Social Media Platforms ◽

And Control ◽

The Impact

Background With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. Objective The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. Methods Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. Results Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. Conclusions Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

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Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02451-9 ◽

2021 ◽

Author(s):

Marcel Bengs ◽

Finn Behrendt ◽

Julia Krüger ◽

Roland Opfer ◽

Alexander Schlaefer

Keyword(s):

Deep Learning ◽

Brain Mri ◽

Magnetic Resonance Images ◽

Spatial Context ◽

Data Sets ◽

Learning Methods ◽

Data Set ◽

Performance Improvements ◽

Wide Range ◽

The Impact

Abstract Purpose Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited. Methods We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance compared to learning from slices. We evaluate and compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Results Using two publicly available segmentation data sets for evaluation, 3D VAEs outperform their 2D counterpart, highlighting the advantage of volumetric context. Also, our 3D erasing methods allow for further performance improvements. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE. Conclusions We propose 3D deep learning methods for UAD in brain MRI combined with 3D erasing and demonstrate that 3D methods clearly outperform their 2D counterpart for anomaly segmentation. Also, our spatial erasing method allows for further performance improvements and reduces the requirement for large data sets.

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Benchmarking Time-Series Data Discretization on Inference Methods

10.1101/378620 ◽

2018 ◽

Author(s):

Yuezhe Li ◽

Tiffany Jann ◽

Paola Vera-Licona

Keyword(s):

Time Series ◽

Reverse Engineering ◽

Time Series Data ◽

Series Data ◽

Discretization Method ◽

Underlying Assumption ◽

Discretization Methods ◽

Inference Methods ◽

Data Discretization ◽

The Impact

AbstractThe rapid development in quantitatively measuring DNA, RNA, and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider 4 published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods’ performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed.AvailabilityAll the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Licona’s lab Github repository: https://github.com/VeraLiconaResearchGroup/Benchmarking_TSDiscretizations

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Impact of outward FDI on firms’ productivity over the food industry: evidence from China

China Agricultural Economic Review ◽

10.1108/caer-12-2017-0246 ◽

2019 ◽

Vol 11 (4) ◽

pp. 655-671 ◽

Cited By ~ 2

Author(s):

Ting-gui Chen ◽

Gan Lin ◽

Mitsuyasu Yabe

Keyword(s):

Food Industry ◽

Outward Fdi ◽

Data Set ◽

Tax Havens ◽

Content Type ◽

Effect Of Food ◽

Micro Level ◽

And Control ◽

The Impact ◽

Firm Effect

Purpose The purpose of this paper is to study the impact of outward foreign direct investment (OFDI) on the productivity of parent firms over the food industry. Design/methodology/approach The main data in this paper are derived from the China Industrial Enterprise Database 2005–2013 and a data set of Chinese firms’ OFDI information. Then this paper uses propensity score matching to match the treatment and control groups with firm characteristics and combines that with the differences-in-differences method to estimate the real effect of OFDI on total factor productivity. Findings The food firm’s OFDI significantly improves the parent firm’s productivity (known as the OFDI own-firm effect), but this promotion only exists in the short term. The OFDI own-firm effect of food firms differs remarkably as the sub-sectors, regions and ownership of firms vary. The food firm’s OFDI in “non-tax havens” and high-income destinations has a significantly stronger effect on the parent firm’s productivity. FDI, R&D and exporting can effectively strengthen the OFDI own-firm effect of food firms. Originality/value The effect of OFDI on food industry productivity has not been researched yet. This paper aims to fill this gap. This paper further divides the characteristics of food firms into different sub-sectors, regions and ownership types for a comparative analysis, with the aim of conducting a more comprehensive study at the micro-level of firms. In addition, an investigation into which factors influence the degree of the OFDI own-firm effect at the micro-level has not been found in the literature. This paper will draw its own conclusions.

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Benchmarking time-series data discretization on inference methods

Bioinformatics ◽

10.1093/bioinformatics/btz036 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3102-3109 ◽

Cited By ~ 1

Author(s):

Yuezhe Li ◽

Tiffany Jann ◽

Paola Vera-Licona

Keyword(s):

Time Series ◽

Reverse Engineering ◽

Time Series Data ◽

Supplementary Information ◽

Series Data ◽

Discretization Method ◽

Discretization Methods ◽

Inference Methods ◽

Data Discretization ◽

The Impact

AbstractSummaryThe rapid development in quantitatively measuring DNA, RNA and protein has generated a great interest in the development of reverse-engineering methods, that is, data-driven approaches to infer the network structure or dynamical model of the system. Many reverse-engineering methods require discrete quantitative data as input, while many experimental data are continuous. Some studies have started to reveal the impact that the choice of data discretization has on the performance of reverse-engineering methods. However, more comprehensive studies are still greatly needed to systematically and quantitatively understand the impact that discretization methods have on inference methods. Furthermore, there is an urgent need for systematic comparative methods that can help select between discretization methods. In this work, we consider four published intracellular networks inferred with their respective time-series datasets. We discretized the data using different discretization methods. Across all datasets, changing the data discretization to a more appropriate one improved the reverse-engineering methods’ performance. We observed no universal best discretization method across different time-series datasets. Thus, we propose DiscreeTest, a two-step evaluation metric for ranking discretization methods for time-series data. The underlying assumption of DiscreeTest is that an optimal discretization method should preserve the dynamic patterns observed in the original data across all variables. We used the same datasets and networks to show that DiscreeTest is able to identify an appropriate discretization among several candidate methods. To our knowledge, this is the first time that a method for benchmarking and selecting an appropriate discretization method for time-series data has been proposed.Availability and implementationAll the datasets, reverse-engineering methods and source code used in this paper are available in Vera-Licona’s lab Github repository: https://github.com/VeraLiconaResearchGroup/Benchmarking_TSDiscretizations.Supplementary informationSupplementary data are available at Bioinformatics online.

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Dataset Size Sensitivity Analysis of Machine Learning Classifiers to Differentiate Molecular Markers of Pediatric Low-Grade Gliomas Based on MRI

10.21203/rs.3.rs-883606/v1 ◽

2021 ◽

Author(s):

Matthias W. Wagner ◽

Khashayar Namdar ◽

Abdullah Alqabbani ◽

Nicolin Hainc ◽

Liana Nobre Figuereido ◽

...

Keyword(s):

Machine Learning ◽

Sample Size ◽

Brain Mri ◽

Training Data ◽

Low Grade ◽

Validation Data ◽

Data Set ◽

Low Grade Gliomas ◽

Braf Fusion ◽

The Impact

Abstract Machine learning (ML) approaches can predict BRAF status of pediatric low-grade gliomas (pLGG) on pre-therapeutic brain MRI. The impact of training data sample size and type of ML model is not established. In this bi-institutional retrospective study, 251 pLGG FLAIR MRI datasets from 2 children’s hospitals were included. Radiomics features were extracted from tumor segmentations and five models (Random Forest, XGBoost, Neural Network (NN) 1 (100:20:2), NN2 (50:10:2), NN3 (50:20:10:2)) were tested to classify them. Classifiers were cross-validated on data from institution 1 and validated on data from institution 2. Starting with 10% of the training data, models were cross-validated using a 4-fold approach at every step with an additional 2.25% increase in sample size. Two-hundred-twenty patients (mean age 8.53 ± 4.94 years, 114 males, 67% BRAF fusion) were included in the training dataset, and 31 patients (mean age 7.97±6.20 years, 18 males, 77% BRAF fusion) in the independent test dataset. NN1 (100:20:2) yielded the highest area under the receiver operating characteristic curve (AUC). It predicted BRAF status with a mean AUC of 0.85, 95% CI [0.83, 0.87] using 60% of the training data and with mean AUC of 0.83, 95% CI [0.82, 0.84] on the independent validation data set.

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Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study (Preprint)

10.2196/preprints.22624 ◽

2020 ◽

Author(s):

Ranganathan Chandrasekaran ◽

Vikalp Mehta ◽

Tejali Valkunde ◽

Evangelos Moustakas

Keyword(s):

Health Care ◽

English Language ◽

Latent Dirichlet Allocation ◽

Data Sets ◽

Data Set ◽

Time Period ◽

Combining Data ◽

Social Media Platforms ◽

And Control ◽

The Impact

BACKGROUND With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. OBJECTIVE The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. METHODS Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. RESULTS Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. CONCLUSIONS Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

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Using social media data to assess the impact of COVID-19 on mental health in China

10.21203/rs.3.rs-100099/v1 ◽

2020 ◽

Author(s):

Yongjian Zhu ◽

Jingui Xie ◽

Yugang Yu ◽

Anfan Chen

Keyword(s):

Mental Health ◽

Health Status ◽

Mental Health Problems ◽

Health Impact ◽

Mental Health Status ◽

Significant Deterioration ◽

Data Set ◽

Mental Health In China ◽

And Control ◽

The Impact

Abstract The outbreak and rapid spread of COVID-19 not only caused an adverse impact on physical health but also brought about mental health problems among the public. To assess the causal impact of COVID-19 on psychological changes in China, we constructed a city-level panel data set based on the expressed sentiment in the contents of 13 million geotagged tweets on Sina Weibo, the Chinese largest microblog platform. Applying a difference-in-differences approach, we found a significant deterioration in mental health status after the occurrence of COVID-19. We also observed that this psychological effect faded out over time during our study period and was more pronounced among women, teenagers and older adults. The mental health impact was more likely to be observed in cities with low levels of initial mental health status, economic development, medical resources, and social security. Our findings may contribute to the understanding and control of COVID-19’s mental health impact.

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Suppressed, but Not Forgotten

Swiss Journal of Psychology ◽

10.1024/1421-0185/a000033 ◽

2011 ◽

Vol 70 (1) ◽

pp. 5-11 ◽

Cited By ~ 8

Author(s):

Beat Meier ◽

Anja König ◽

Samuel Parak ◽

Katharina Henke

Keyword(s):

Memory Trace ◽

Thought Suppression ◽

Test Phase ◽

Indirect Test ◽

Final Test ◽

Test Experiment ◽

And Control ◽

Cue Words ◽

The Impact

This study investigates the impact of thought suppression over a 1-week interval. In two experiments with 80 university students each, we used the think/no-think paradigm in which participants initially learn a list of word pairs (cue-target associations). Then they were presented with some of the cue words again and should either respond with the target word or avoid thinking about it. In the final test phase, their memory for the initially learned cue-target pairs was tested. In Experiment 1, type of memory test was manipulated (i.e., direct vs. indirect). In Experiment 2, type of no-think instructions was manipulated (i.e., suppress vs. substitute). Overall, our results showed poorer memory for no-think and control items compared to think items across all experiments and conditions. Critically, however, more no-think than control items were remembered after the 1-week interval in the direct, but not in the indirect test (Experiment 1) and with thought suppression, but not thought substitution instructions (Experiment 2). We suggest that during thought suppression a brief reactivation of the learned association may lead to reconsolidation of the memory trace and hence to better retrieval of suppressed than control items in the long term.

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Research Participation Experiences of Informants of Suicide and Control Cases

Crisis ◽

10.1027/0227-5910/a000025 ◽

2010 ◽

Vol 31 (5) ◽

pp. 238-246 ◽

Cited By ~ 9

Author(s):

Paul W. C. Wong ◽

Wincy S. C. Chan ◽

Philip S. L. Beh ◽

Fiona W. S. Yau ◽

Paul S. F. Yip ◽

...

Keyword(s):

Ethical Issues ◽

Research Participation ◽

Self Report ◽

Initial Contact ◽

Psychological Autopsy ◽

Autopsy Study ◽

The People ◽

And Control ◽

The Impact

Background: Ethical issues have been raised about using the psychological autopsy approach in the study of suicide. The impact on informants of control cases who participated in case-control psychological autopsy studies has not been investigated. Aims: (1) To investigate whether informants of suicide cases recruited by two approaches (coroners’ court and public mortuaries) respond differently to the initial contact by the research team. (2) To explore the reactions, reasons for participation, and comments of both the informants of suicide and control cases to psychological autopsy interviews. (3) To investigate the impact of the interviews on informants of suicide cases about a month after the interviews. Methods: A self-report questionnaire was used for the informants of both suicide and control cases. Telephone follow-up interviews were conducted with the informants of suicide cases. Results: The majority of the informants of suicide cases, regardless of the initial route of contact, as well as the control cases were positive about being approached to take part in the study. A minority of informants of suicide and control cases found the experience of talking about their family member to be more upsetting than expected. The telephone follow-up interviews showed that none of the informants of suicide cases reported being distressed by the psychological autopsy interviews. Limitations: The acceptance rate for our original psychological autopsy study was modest. Conclusions: The findings of this study are useful for future participants and researchers in measuring the potential benefits and risks of participating in similar sensitive research. Psychological autopsy interviews may be utilized as an active engagement approach to reach out to the people bereaved by suicide, especially in places where the postvention work is underdeveloped.

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