scholarly journals ID-Seg: An Accurate and Reliable Infant Deep learning Segmentation Framework for Limbic Structures

2021 ◽  
Author(s):  
Yun Wang ◽  
Fateme Sadat Haghpanah ◽  
Xuzhe Zhang ◽  
Katie Santamaria ◽  
Gabriela Koch da Costa Aguiar Alves ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Magnetic Resonance Imaging ◽  
Limbic Structure ◽  
Segmentation Evaluation ◽  
Invasive Approach ◽  
Limbic Structures ◽  
Neuropsychological Development ◽  
Segmentation Framework ◽  
The Impact

Early post-natal period brain magnetic resonance imaging (MRI) is becoming a common non-invasive approach to characterize the impact of prenatal exposures on neurodevelopment and to investigate early biomarkers for risk. Limbic structures are particular of interest in psychiatric disorder related research. Despite the promise of infant neuroimaging and the success of initial infant MRI studies, assessing limbic structure and function remains a significant challenge due to low inter-regional intensity contrast and high curvature (e.g. hippocampus). Of note, the agreement between existing automatic techniques and manual segmentation remains either untested or poor particularly for the amygdala and hippocampus. In this work, we developed an accurate (based on three segmentation evaluation metrics), reliable and efficient infant deep learning segmentation framework (ID−Seg) to address the aforementioned challenges. Specifically, we leveraged a large dataset of 473 infant MRI scans to train ID−Seg and then evaluated ID−Seg performance on internal (n=20) and external datasets (n=10) with manual segmentations. Compared with a state-of-the-art segmentation pipeline, we demonstrated that ID−Seg significantly improved the segmentation accuracy of limbic structures (hippocampus and amygdala) in newborn infants. Moreover, in a small, proof−of−concept analysis, we found that ID-Seg derived morphometric measures yield strong brain−behavior associations. As such, our ID-Seg may improve our capacity to efficiently measure MRI−based brain features relevant to neuropsychological development, and ultimately advance the success of quantitative analyses on large-scale datasets.

scholarly journals Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020 ◽  
Vol 12 (18) ◽  
pp. 3053 ◽  
Author(s):  
Thorsten Hoeser ◽  
Felix Bachofer ◽  
Claudia Kuenzer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Object Detection ◽  
Land Surface ◽  
Large Scale ◽  
Earth Observation ◽  
Observation Data ◽  
Fine Grained ◽  
The Impact ◽  
Object Dynamics

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

scholarly journals Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shitao Zhao ◽  
Michiaki Hamada
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Binding Sites ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Rna Binding ◽  
Binding Prediction ◽  
Binding Preference ◽  
The Impact

Abstract Background Protein-RNA interactions play key roles in many processes regulating gene expression. To understand the underlying binding preference, ultraviolet cross-linking and immunoprecipitation (CLIP)-based methods have been used to identify the binding sites for hundreds of RNA-binding proteins (RBPs) in vivo. Using these large-scale experimental data to infer RNA binding preference and predict missing binding sites has become a great challenge. Some existing deep-learning models have demonstrated high prediction accuracy for individual RBPs. However, it remains difficult to avoid significant bias due to the experimental protocol. The DeepRiPe method was recently developed to solve this problem via introducing multi-task or multi-label learning into this field. However, this method has not reached an ideal level of prediction power due to the weak neural network architecture. Results Compared to the DeepRiPe approach, our Multi-resBind method demonstrated substantial improvements using the same large-scale PAR-CLIP dataset with respect to an increase in the area under the receiver operating characteristic curve and average precision. We conducted extensive experiments to evaluate the impact of various types of input data on the final prediction accuracy. The same approach was used to evaluate the effect of loss functions. Finally, a modified integrated gradient was employed to generate attribution maps. The patterns disentangled from relative contributions according to context offer biological insights into the underlying mechanism of protein-RNA interactions. Conclusions Here, we propose Multi-resBind as a new multi-label deep-learning approach to infer protein-RNA binding preferences and predict novel interactions. The results clearly demonstrate that Multi-resBind is a promising tool to predict unknown binding sites in vivo and gain biology insights into why the neural network makes a given prediction.

The impact of gravity wave drag modifications on stratospheric pathways of Arctic-midlatitude linkages using deep learning

2021 ◽  
Author(s):  
Sina Mehrdad ◽  
Khalil Karami ◽  
Dörthe Handorf ◽  
Johannes Quaas ◽  
Ines Höschel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Gravity Wave ◽  
Large Scale ◽  
Lower Boundary ◽  
Polar Vortex ◽  
Wave Drag ◽  
The Arctic ◽  
External Forcing ◽  
Arctic Amplification ◽  
The Impact

<p>The global warming has been observed to be more severe in the Arctic compared to the rest of the world. This enhanced warming i.e. Arctic Amplification is not just the result of local feedback processes in the Arctic. The stratospheric pathways of Arctic-midlatitude linkages and large-scale dynamical processes can contribute to the Arctic Amplification. The polar stratospheric dynamics crucially depends on the atmospheric waves at all scales. The winter polar vortex can be disturbed by gravity waves in the middle atmosphere. To investigate the sensitivity of the polar vortex dynamics, large-scale dynamical processes, and the stratospheric pathways of the Arctic-midlatitude linkages to the modification of gravity wave drag, we conduct sensitivity experiments using the global atmospheric model ICON-NWP (ICOsahedral Nonhydrostatic Model for Numerical Weather Prediction). These sensitivity experiments are performed by imposing a repeated annual cycle of the year 1986 for sea surface temperatures and sea ice as lower boundary conditions and for greenhouse gas concentrations as external forcing. This year is selected as both El-Nino Southern Oscillation and Pacific decadal oscillation were in their neutral phase and no explosive volcanic eruption has occurred. Hence, lower boundary and external forcing conditions in this year can serve as a useful proxy for the multi-year mean condition and an estimate of its internal variability. We performed simulations where in the control simulation the sub-grid parameterization scheme for both orographic and non-orographic gravity wave drags are switched on. The other two experiments are identical to the control simulation except that either orographic or non-orographic gravity wave drags are switched off.</p> <p>Recently, deep learning has extraordinarily progressed our ability to recognize complex patterns in big datasets. Deep neural networks have shown great capabilities to capture the dynamical process of the atmosphere. Applying deep learning algorithms on experiments’ results, the impact of gravity wave drag modifications on large-scale mechanisms of the Arctic Amplification will be analyzed. Special emphasis will be put on the effects of gravity wave drag modifications on the polar vortex dynamics.</p>

scholarly journals Improving variant calling using population data and deep learning

2021 ◽  
Author(s):  
Nae-Chyun Chen ◽  
Alexey Kolesnikov ◽  
Sidharth Goel ◽  
Taedong Yun ◽  
Pi-Chuan Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Variant Calling ◽  
Population Data ◽  
Training Data ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Population Information ◽  
Scale Population ◽  
The Impact

Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we modify DeepVariant to add a new channel encoding population allele frequencies from the 1000 Genomes Project. We show that this model reduces variant calling errors, improving both precision and recall. We assess the impact of using population-specific or diverse reference panels. We achieve the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

Impact of COVID-19 on Nuclear Medicine in Germany, Austria and Switzerland: An International Survey in April 2020

2020 ◽  
Vol 59 (04) ◽  
pp. 294-299 ◽  
Author(s):  
Lutz S. Freudenberg ◽  
Ulf Dittmer ◽  
Ken Herrmann
Keyword(s):  
Nuclear Medicine ◽  
Health Systems ◽  
Personal Protective Equipment ◽  
Large Scale ◽  
Diagnostic Procedures ◽  
Protective Equipment ◽  
Current Crisis ◽  
Web Based ◽  
Public Versus Private ◽  
The Impact

Abstract Introduction Preparations of health systems to accommodate large number of severely ill COVID-19 patients in March/April 2020 has a significant impact on nuclear medicine departments. Materials and Methods A web-based questionnaire was designed to differentiate the impact of the pandemic on inpatient and outpatient nuclear medicine operations and on public versus private health systems, respectively. Questions were addressing the following issues: impact on nuclear medicine diagnostics and therapy, use of recommendations, personal protective equipment, and organizational adaptations. The survey was available for 6 days and closed on April 20, 2020. Results 113 complete responses were recorded. Nearly all participants (97 %) report a decline of nuclear medicine diagnostic procedures. The mean reduction in the last three weeks for PET/CT, scintigraphies of bone, myocardium, lung thyroid, sentinel lymph-node are –14.4 %, –47.2 %, –47.5 %, –40.7 %, –58.4 %, and –25.2 % respectively. Furthermore, 76 % of the participants report a reduction in therapies especially for benign thyroid disease (-41.8 %) and radiosynoviorthesis (–53.8 %) while tumor therapies remained mainly stable. 48 % of the participants report a shortage of personal protective equipment. Conclusions Nuclear medicine services are notably reduced 3 weeks after the SARS-CoV-2 pandemic reached Germany, Austria and Switzerland on a large scale. We must be aware that the current crisis will also have a significant economic impact on the healthcare system. As the survey cannot adapt to daily dynamic changes in priorities, it serves as a first snapshot requiring follow-up studies and comparisons with other countries and regions.

HOME VISITS DURING THE FIRST MONTH OF DELIVERY FOR RECENTLY DELIVERED WOMEN IN UTTAR PRADESH, INDIA

2020 ◽  
Vol 6 (5) ◽  
pp. 1183-1189
Author(s):  
Dr. Tridibesh Tripathy ◽  
Dr. Umakant Prusty ◽  
Dr. Chintamani Nayak ◽  
Dr. Rakesh Dwivedi ◽  
Dr. Mohini Gautam
Keyword(s):  
Large Scale ◽  
National Level ◽  
Uttar Pradesh ◽  
Life Time ◽  
Structured Interview ◽  
The Other ◽  
Registration System ◽  
Action Group ◽  
Current Article ◽  
The Impact

The current article of Uttar Pradesh (UP) is about the ASHAs who are the daughters-in-law of a family that resides in the same community that they serve as the grassroots health worker since 2005 when the NRHM was introduced in the Empowered Action Group (EAG) states. UP is one such Empowered Action Group (EAG) state. The current study explores the actual responses of Recently Delivered Women (RDW) on their visits during the first month of their recent delivery. From the catchment area of each of the 250 ASHAs, two RDWs were selected who had a child in the age group of 3 to 6 months during the survey. The response profiles of the RDWs on the post- delivery first month visits are dwelled upon to evolve a picture representing the entire state of UP. The relevance of the study assumes significance as detailed data on the modalities of postnatal visits are available but not exclusively for the first month period of their recent delivery. The details of the post-delivery first month period related visits are not available even in large scale surveys like National Family Health Survey 4 done in 2015-16. The current study gives an insight in to these visits with a five-point approach i.e. type of personnel doing the visit, frequency of the visits, visits done in a particular week from among those four weeks separately for the three visits separately. The current study is basically regarding the summary of this Penta approach for the post- delivery one-month period.     The first month period after each delivery deals with 70% of the time of the postnatal period & the entire neonatal period. Therefore, it does impact the Maternal Mortality Rate & Ratio (MMR) & the Neonatal Mortality Rates (NMR) in India and especially in UP through the unsafe Maternal & Neonatal practices in the first month period after delivery. The current MM Rate of UP is 20.1 & MM Ratio is 216 whereas the MM ratio is 122 in India (SRS, 2019). The Sample Registration System (SRS) report also mentions that the Life Time Risk (LTR) of a woman in pregnancy is 0.7% which is the highest in the nation (SRS, 2019). This means it is very risky to give birth in UP in comparison to other regions in the country (SRS, 2019). This risk is at the peak in the first month period after each delivery. Similarly, the current NMR in India is 23 per 1000 livebirths (UNIGME,2018). As NMR data is not available separately for states, the national level data also hold good for the states and that’s how for the state of UP as well. These mortalities are the impact indicators and such indicators can be reduced through long drawn processes that includes effective and timely visits to RDWs especially in the first month period after delivery. This would help in making their post-natal & neonatal stage safe. This is the area of post-delivery first month visit profile detailing that the current article helps in popping out in relation to the recent delivery of the respondents.   A total of four districts of Uttar Pradesh were selected purposively for the study and the data collection was conducted in the villages of the respective districts with the help of a pre-tested structured interview schedule with both close-ended and open-ended questions.  The current article deals with five close ended questions with options, two for the type of personnel & frequency while the other three are for each of the three visits in the first month after the recent delivery of respondents. In addition, in-depth interviews were also conducted amongst the RDWs and a total 500 respondents had participated in the study.   Among the districts related to this article, the results showed that ASHA was the type of personnel who did the majority of visits in all the four districts. On the other hand, 25-40% of RDWs in all the 4 districts replied that they did not receive any visit within the first month of their recent delivery. Regarding frequency, most of the RDWs in all the 4 districts received 1-2 times visits by ASHAs.   Regarding the first visit, it was found that the ASHAs of Barabanki and Gonda visited less percentage of RDWs in the first week after delivery. Similarly, the second visit revealed that about 1.2% RDWs in Banda district could not recall about the visit. Further on the second visit, the RDWs responded that most of them in 3 districts except Gonda district did receive the second postnatal visit in 7-15 days after their recent delivery. Less than half of RDWs in Barabanki district & just more than half of RDWs in Gonda district received the third visit in 15-21 days period after delivery. For the same period, the majority of RDWs in the rest two districts responded that they had been entertained through a home visit.

The Impact of Quantitative Easing on Emerging Markets – Literature Review

e-Finanse ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 67-76
Author(s):  
Piotr Bartkiewicz
Keyword(s):  
Emerging Markets ◽  
Large Scale ◽  
Empirical Literature ◽  
Quantitative Easing ◽  
Methodological Choices ◽  
The Subject ◽  
Methodological Approaches ◽  
The Impact ◽  
Sufficient Precision

AbstractThe article presents the results of the review of the empirical literature regarding the impact of quantitative easing (QE) on emerging markets (EMs). The subject is of interest to policymakers and researchers due to the increasingly larger role of EMs in the world economy and the large-scale capital flows occurring after 2009. The review is conducted in a systematic manner and takes into consideration different methodological choices, samples and measurement issues. The paper puts the summarized results in the context of transmission channels identified in the literature. There are few distinct methodological approaches present in the literature. While there is a consensus regarding the direction of the impact of QE on EMs, its size and durability have not yet been assessed with sufficient precision. In addition, there are clear gaps in the empirical findings, not least related to relative underrepresentation of the CEE region (in particular, Poland).

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