scholarly journals Functional MRI in awake dogs predicts suitability for assistance work

2016 ◽  
Author(s):  
Gregory S. Berns ◽  
Andrew M. Brooks ◽  
Mark Spivak ◽  
Kerinne Levy
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Differential Response ◽  
Successful Outcome ◽  
Service Dogs ◽  
Service Dog ◽  
Diagnostic Capability ◽  
Service Training ◽  
The Stranger ◽  
Fold Cross Validation

ABSTRACTThe overall goal of this work was to measure the efficacy of fMRI for predicting whether a dog would be a successful service dog. The training and imaging were performed in 50 dogs entering advanced training at 17-21 months of age. FMRI responses were measured while each dog observed hand signals indicating either reward or no reward and given by both a familiar handler and a stranger. 49 dogs successfully completed fMRI training and scanning. Of these, 33 eventually completed service training and were matched with a person, while 10 were released for behavioral reasons. Using anatomically defined regions-of-interest in the ventral caudate, amygdala, and visual cortex, we developed a classifier based on the dogs' outcomes. We found that responses in the stranger condition were sufficient to develop an accurate brain-based classifier. On all data, the classifier had a positive predictive value of 96% with 10% false positives. The area under the receiver operating characteristic curve was 0.90 (0.79 with 4-fold cross-validation, P=0.02), indicating a significant diagnostic capability. Within the stranger condition, the differential response to [reward – no reward] in ventral caudate was positively correlated with a successful outcome, while the differential response in the amygdala was negatively correlated to outcome. These results show that successful service dogs transfer knowledge to strangers as indexed by ventral caudate activity without excessive arousal as measured in the amygdala.

scholarly journals Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests

2019 ◽  
Vol 34 (6) ◽  
pp. 2017-2044 ◽  
Author(s):  
Eric D. Loken ◽  
Adam J. Clark ◽  
Amy McGovern ◽  
Montgomery Flora ◽  
Kent Knopfmeier
Keyword(s):  
Random Forest ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Ensemble Forecast ◽  
Grid Spacing ◽  
Relative Operating Characteristic ◽  
Advantages And Disadvantages ◽  
Skill Scores ◽  
Systematic Biases ◽  
Fold Cross Validation

Abstract Most ensembles suffer from underdispersion and systematic biases. One way to correct for these shortcomings is via machine learning (ML), which is advantageous due to its ability to identify and correct nonlinear biases. This study uses a single random forest (RF) to calibrate next-day (i.e., 12–36-h lead time) probabilistic precipitation forecasts over the contiguous United States (CONUS) from the Short-Range Ensemble Forecast System (SREF) with 16-km grid spacing and the High-Resolution Ensemble Forecast version 2 (HREFv2) with 3-km grid spacing. Random forest forecast probabilities (RFFPs) from each ensemble are compared against raw ensemble probabilities over 496 days from April 2017 to November 2018 using 16-fold cross validation. RFFPs are also compared against spatially smoothed ensemble probabilities since the raw SREF and HREFv2 probabilities are overconfident and undersample the true forecast probability density function. Probabilistic precipitation forecasts are evaluated at four precipitation thresholds ranging from 0.1 to 3 in. In general, RFFPs are found to have better forecast reliability and resolution, fewer spatial biases, and significantly greater Brier skill scores and areas under the relative operating characteristic curve compared to corresponding raw and spatially smoothed ensemble probabilities. The RFFPs perform best at the lower thresholds, which have a greater observed climatological frequency. Additionally, the RF-based postprocessing technique benefits the SREF more than the HREFv2, likely because the raw SREF forecasts contain more systematic biases than those from the raw HREFv2. It is concluded that the RFFPs provide a convenient, skillful summary of calibrated ensemble output and are computationally feasible to implement in real time. Advantages and disadvantages of ML-based postprocessing techniques are discussed.

scholarly journals Peripapillary retinal artery in first diagnosed and untreated normal tension glaucoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Xin Rong ◽  
Yu Cai ◽  
Mei Li ◽  
Yuan Fang ◽  
Tian Tian ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Normal Tension Glaucoma ◽  
Newly Diagnosed ◽  
Fiber Layer ◽  
Nerve Fiber Layer Thickness ◽  
Diagnostic Capability ◽  
Retinal Artery ◽  
Operating Characteristic Curve ◽  
Retinal Arteriolar

Abstract Background Glaucoma, an important cause of visual impairment in many countries, remains a common eye condition due to difficulties in its early diagnosis. We analyzed the characteristics of retinal arteries to add a valuable technology for helping the normal tension glaucoma (NTG) diagnosis. Methods This study included 51 patients with newly diagnosed NTG with hemifield defects and 60 age-matched controls. Peripapillary retinal arteriolar calibers (PRACs) photoed by non-mydriatic retinal camera were measured using ImageJ by two masked readers. We also performed spectral-domain optical coherence tomography to evaluate retinal nerve fiber layer thickness (RNFLT) and optic disc parameters. Their relations to retinal arteriolar calibers were investigated by univariate and multivariate linear regression. The area under the receiver operating characteristic curve (AUROC) was used to confirm the powers to detect NTG by PRACs. Results PRACs in four quadrants were significantly reduced in individuals with first diagnosed NTG (82 ± 15.1 μm, 80 ± 13.6 μm, 71 ± 11.6 μm, and 64 ± 10.0 μm) compared with those in age-matched controls (101 ± 9.8 μm, 105 ± 8.7 μm, 90 ± 7.5 μm, and 82 ± 9.8 μm). Superotemporal and inferotemporal PRACs in the visual field-affected hemifield were narrower than those in the unaffected hemifield in NTG group (P ≤ 0.004). Temporal PRACs in the RNFL unaffected hemifield were significantly narrower than in healthy eyes (P < 0.001). Superotemporal PRAC showed a significant correlation with superior RNFLT (β = 0.659, P < 0.001), and a similar relationship was found between inferotemporal PRAC and inferior RNFLT (β = 0.227, P = 0.015). The diagnostic capability of temporal PRACs was satisfactory (superotemporal PRAC; AUROC 0.983, cut-off value 84.7 μm, inferotemporal PRAC; AUROC 0.946, cut-off value 94.2 μm). Conclusions PRAC and inferotemporal PRAC are valid parameters for discriminating patients with NTG.

scholarly journals Peripapillary retinal artery in first diagnosed and untreated normal tension glaucoma

2019 ◽  
Author(s):  
XIN RONG ◽  
Yu Cai ◽  
Mei Li ◽  
Yuan Fang ◽  
Tian Tian ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Normal Tension Glaucoma ◽  
Newly Diagnosed ◽  
Fiber Layer ◽  
Nerve Fiber Layer Thickness ◽  
Diagnostic Capability ◽  
Retinal Artery ◽  
Operating Characteristic Curve ◽  
Retinal Arteriolar

Abstract Background Glaucoma, an important cause of visual impairment in many countries, remains a common eye condition due to difficulties in its early diagnosis. We analyzed the characteristics of retinal arteries to add a valuable technology for helping the normal tension glaucoma (NTG) diagnosis. Methods This study included 51 patients with newly diagnosed NTG with hemifield defects and 60 age-matched controls. Peripapillary retinal arteriolar calibers (PRACs) photoed by non-mydriatic retinal camera were measured using ImageJ by two masked readers. We also performed spectral-domain optical coherence tomography to evaluate retinal nerve fiber layer thickness (RNFLT) and optic disc parameters. Their relations to retinal arteriolar calibers were investigated by univariate and multivariate linear regression. The area under the receiver operating characteristic curve (AUROC) was used to confirm the powers to detect NTG by PRACs. Results PRACs in four quadrants were significantly reduced in individuals with first diagnosed NTG (82 ± 15.1 μm, 80 ± 13.6 μm, 71 ± 11.6 μm, and 64 ±10.0 μm) compared with those in age-matched controls (101 ± 9.8 μm, 105 ± 8.7 μm, 90 ± 7.5 μm, and 82 ± 9.8 μm). Superotemporal and inferotemporal PRACs in the visual field-affected hemifield were narrower than those in the unaffected hemifield in NTG group ( P ≤0.004). Temporal PRACs in the RNFL unaffected hemifield were significantly narrower than in healthy eyes ( P <0.001). Superotemporal PRAC showed a significant correlation with superior RNFLT (β=0.659, P <0.001), and a similar relationship was found between inferotemporal PRAC and inferior RNFLT (β=0.227, P =0.015). The diagnostic capability of temporal PRACs was satisfactory (superotemporal PRAC; AUROC 0.983, cut-off value 84.7 μm, inferotemporal PRAC; AUROC 0.946, cut-off value 94.2 μm). Conclusions PRAC and infero temporal PRAC are valid parameters for discriminating patients with NTG.

scholarly journals Peripapillary retinal artery in first diagnosed and untreated normal tension glaucoma

2019 ◽  
Author(s):  
XIN RONG ◽  
Yu Cai ◽  
Mei Li ◽  
Yuan Fang ◽  
Tian Tian ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Normal Tension Glaucoma ◽  
Newly Diagnosed ◽  
Fiber Layer ◽  
Nerve Fiber Layer Thickness ◽  
Diagnostic Capability ◽  
Retinal Artery ◽  
Operating Characteristic Curve ◽  
Retinal Arteriolar

Abstract Background Glaucoma, an important cause of visual impairment in many countries, remains a common eye condition due to difficulties in its early diagnosis. We analyzed the characteristics of retinal arteries to add a valuable technology for helping the normal tension glaucoma (NTG) diagnosis. Methods This study included 51 patients with newly diagnosed NTG with hemifield defects and 60 age-matched controls. Peripapillary retinal arteriolar calibers (PRACs) photoed by non-mydriatic retinal camera were measured using ImageJ by two masked readers. We also performed spectral-domain optical coherence tomography to evaluate retinal nerve fiber layer thickness (RNFLT) and optic disc parameters. Their relations to retinal arteriolar calibers were investigated by univariate and multivariate linear regression. The area under the receiver operating characteristic curve (AUROC) was used to confirm the powers to detect NTG by PRACs. Results PRACs in four quadrants were significantly reduced in individuals with first diagnosed NTG (82 ± 15.1 μm, 80 ± 13.6 μm, 71 ± 11.6 μm, and 64 ±10.0 μm) compared with those in age-matched controls (101 ± 9.8 μm, 105 ± 8.7 μm, 90 ± 7.5 μm, and 82 ± 9.8 μm). Superotemporal and inferotemporal PRACs in the visual field-affected hemifield were narrower than those in the unaffected hemifield in NTG group ( P ≤0.004). Temporal PRACs in the RNFL unaffected hemifield were significantly narrower than in healthy eyes ( P <0.001). Superotemporal PRAC showed a significant correlation with superior RNFLT (β=0.659, P <0.001), and a similar relationship was found between inferotemporal PRAC and inferior RNFLT (β=0.227, P =0.015). The diagnostic capability of temporal PRACs was satisfactory (superotemporal PRAC; AUROC 0.983, cut-off value 84.7 μm, inferotemporal PRAC; AUROC 0.946, cut-off value 94.2 μm). Conclusions PRAC and infero temporal PRAC are valid parameters for discriminating patients with NTG.

Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach

2020 ◽  
Author(s):  
Yu Zhang ◽  
Cangzhi Jia ◽  
Chee Keong Kwoh
Keyword(s):  
Deep Learning ◽  
Molecular Mechanisms ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Published Data ◽  
Different Types ◽  
Precision Recall Curve ◽  
Deep Learning Model ◽  
Fold Cross Validation

Abstract Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA mainly rely on calculating the similarity of lncRNA or investigating whether an lncRNA can interact with a specific biomolecule or disease. In this work, we explored the functions of lncRNA from a different perspective: we presented a tool for predicting the interaction biomolecule type for a given lncRNA. For this purpose, we first investigated the main molecular mechanisms of the interactions of lncRNA–RNA, lncRNA–protein and lncRNA–DNA. Then, we developed an ensemble deep learning model: lncIBTP (lncRNA Interaction Biomolecule Type Prediction). This model predicted the interactions between lncRNA and different types of biomolecules. On the 5-fold cross-validation, the lncIBTP achieves average values of 0.7042 in accuracy, 0.7903 and 0.6421 in macro-average area under receiver operating characteristic curve and precision–recall curve, respectively, which illustrates the model effectiveness. Besides, based on the analysis of the collected published data and prediction results, we hypothesized that the characteristics of lncRNAs that interacted with DNA may be different from those that interacted with only RNA.

scholarly journals Peripapillary retinal artery in first diagnosed and untreated normal tension glaucoma

2019 ◽  
Author(s):  
XIN RONG ◽  
Yu Cai ◽  
Mei Li ◽  
Yuan Fang ◽  
Tian Tian ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Normal Tension Glaucoma ◽  
Newly Diagnosed ◽  
Fiber Layer ◽  
Nerve Fiber Layer Thickness ◽  
Diagnostic Capability ◽  
Retinal Artery ◽  
Operating Characteristic Curve ◽  
Retinal Arteriolar

Abstract Backgroud Glaucoma, an important cause of visual impairment in many countries, remains a common eye condition due to difficulties in its early diagnosis. We analyzed the characteristics of retinal arteries to add a valuable technology for helping the normal tension glaucoma (NTG) diagnosis. Methods This study included 51 patients with newly diagnosed NTG with hemifield defects and 60 age-matched controls. Peripapillary retinal arteriolar calibers (PRACs) photoed by non-mydriatic retinal camera were measured using ImageJ by two masked readers. We also performed spectral-domain optical coherence tomography to evaluate retinal nerve fiber layer thickness (RNFLT) and optic disc parameters. Their relations to retinal arteriolar calibers were investigated by univariate and multivariate linear regression. The area under the receiver operating characteristic curve (AUROC) was used to confirm the powers to detect NTG by PRACs. Results PRACs in four quadrants were significantly reduced in individuals with first diagnosed NTG (82 ± 15.1 μm, 80 ± 13.6 μm, 71 ± 11.6 μm, and 64 ±10.0 μm) compared with those in age-matched controls (101 ± 9.8 μm, 105 ± 8.7 μm, 90 ± 7.5 μm, and 82 ± 9.8 μm). Superotemporal and inferotemporal PRACs in the visual field-affected hemifield were narrower than those in the unaffected hemifield in NTG group (P≤0.004). Temporal PRACs in the RNFL unaffected hemifield were significantly narrower than in healthy eyes (P<0.001). Superotemporal PRAC showed a significant correlation with superior RNFLT (β=0.551, P<0.001), and a similar relationship was found between inferotemporal PRAC and inferior RNFLT (β=0.296, P=0.050). These associations were independent of IOP. The diagnostic capability of temporal PRACs was satisfactory (superotemporal PRAC; AUROC 0.983, cut-off value 84.7 μm, inferotemporal PRAC; AUROC 0.946, cut-off value 94.2 μm). Conclusions Superotemporal PRAC and inferotemporal PRAC are valid parameters for discriminating patients with NTG. And it is likely that narrowed peripapillary vessels preceded the RNFL damage detected by digital retinal camera.

scholarly journals DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2034
Author(s):  
Omneya Attallah
Keyword(s):  
Deep Learning ◽  
Retinopathy Of Prematurity ◽  
Diagnostic Tool ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Diagnostic Tools ◽  
Preterm Neonates ◽  
Hadamard Transform ◽  
Spatial Features ◽  
Diagnostic Capability

Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four convolution neural networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.

scholarly journals Peripapillary retinal artery in first diagnosed and untreated normal tension glaucoma

2019 ◽  
Author(s):  
XIN RONG ◽  
Yu Cai ◽  
Mei Li ◽  
Yuan Fang ◽  
Tian Tian ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Normal Tension Glaucoma ◽  
Newly Diagnosed ◽  
Fiber Layer ◽  
Nerve Fiber Layer Thickness ◽  
Diagnostic Capability ◽  
Retinal Artery ◽  
Operating Characteristic Curve ◽  
Retinal Arteriolar

Abstract Background Glaucoma, an important cause of visual impairment in many countries, remains a common eye condition due to difficulties in its early diagnosis. We analyzed the characteristics of retinal arteries to add a valuable technology for helping the normal tension glaucoma (NTG) diagnosis. Methods This study included 51 patients with newly diagnosed NTG with hemifield defects and 60 age-matched controls. Peripapillary retinal arteriolar calibers (PRACs) photoed by non-mydriatic retinal camera were measured using ImageJ by two masked readers. We also performed spectral-domain optical coherence tomography to evaluate retinal nerve fiber layer thickness (RNFLT) and optic disc parameters. Their relations to retinal arteriolar calibers were investigated by univariate and multivariate linear regression. The area under the receiver operating characteristic curve (AUROC) was used to confirm the powers to detect NTG by PRACs. Results PRACs in four quadrants were significantly reduced in individuals with first diagnosed NTG (82 ± 15.1 μm, 80 ± 13.6 μm, 71 ± 11.6 μm, and 64 ±10.0 μm) compared with those in age-matched controls (101 ± 9.8 μm, 105 ± 8.7 μm, 90 ± 7.5 μm, and 82 ± 9.8 μm). Superotemporal and inferotemporal PRACs in the visual field-affected hemifield were narrower than those in the unaffected hemifield in NTG group ( P ≤0.004). Temporal PRACs in the RNFL unaffected hemifield were significantly narrower than in healthy eyes ( P <0.001). Superotemporal PRAC showed a significant correlation with superior RNFLT (β=0.659, P <0.001), and a similar relationship was found between inferotemporal PRAC and inferior RNFLT (β=0.227, P =0.015). The diagnostic capability of temporal PRACs was satisfactory (superotemporal PRAC; AUROC 0.983, cut-off value 84.7 μm, inferotemporal PRAC; AUROC 0.946, cut-off value 94.2 μm). Conclusions PRAC and infero temporal PRAC are valid parameters for discriminating patients with NTG.

scholarly journals Stacked Generalization: An Introduction to Super Learning

2017 ◽  
Author(s):  
Ashley I. Naimi ◽  
Laura B. Balzer
Keyword(s):  
Operating Characteristic ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Characteristic Curve ◽  
Squared Error ◽  
Stacked Generalization ◽  
Super Learner ◽  
Technical Details ◽  
Fold Cross Validation ◽  
Weighted Combination

AbstractStacked generalization is an ensemble method that allows researchers to combine several different prediction algorithms into one. Since its introduction in the early 1990s, the method has evolved several times into what is now known as “Super Learner”. Super Learner uses V -fold cross-validation to build the optimal weighted combination of predictions from a library of candidate algorithms. Optimality is defined by a user-specified objective function, such as minimizing mean squared error or maximizing the area under the receiver operating characteristic curve. Although relatively simple in nature, use of the Super Learner by epidemiologists has been hampered by limitations in understanding conceptual and technical details. We work step-by-step through two examples to illustrate concepts and address common concerns.

scholarly journals LE-MDCAP: A Computational Model to Prioritize Causal miRNA–Disease Associations

2021 ◽  
Vol 22 (24) ◽  
pp. 13607
Author(s):  
Zhou Huang ◽  
Yu Han ◽  
Leibo Liu ◽  
Qinghua Cui ◽  
Yuan Zhou
Keyword(s):  
Computational Models ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Levenshtein Distance ◽  
Similarity Matrix ◽  
Disease Treatment ◽  
Independent Test ◽  
Disease Associations ◽  
Fold Cross Validation

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA–disease associations, but few models are available to further prioritize causal miRNA–disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA–Disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA–disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA–disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA–disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA–disease associations from general miRNA–disease associations.

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