Olfactory threshold tuning by stimulus ordering

Abstract It is well established that early blindness results in enhancement of the remaining nonvisual sensory modalities accompanied by functional and anatomical brain plasticity. While auditory and tactile functions have been largely investigated, the results regarding olfactory functions remained less explored and less consistent. In the present study, we investigated olfactory function in blind mice using 3 tests: the buried food test, the olfactory threshold test, and the olfactory performance test. The results indicated better performance of blind mice in the buried food test and odor performance test while there was no difference in the olfactory threshold test. Using histological measurements, we also investigated if there was anatomical plasticity in the olfactory bulbs (OB), the most salient site for olfactory processing. The results indicated a larger volume of the OB driven by larger glomerular and granular layers in blind mice compared with sighted mice. Structural plasticity in the OB may underlie the enhanced olfactory performance in blind mice.

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Energy variance criterion and threshold tuning scheme for high impedance fault detection

IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233) ◽

10.1109/pesw.1999.747300 ◽

1999 ◽

Author(s):

Keng-Yu Lien ◽

Shi-Lin Chen ◽

Ching-Jung Liao ◽

Tzong-Yih Guo ◽

Tsair-Ming Lin ◽

...

Keyword(s):

Fault Detection ◽

High Impedance ◽

High Impedance Fault ◽

Threshold Tuning ◽

Tuning Scheme ◽

Variance Criterion

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Energy variance criterion and threshold tuning scheme for high impedance fault detection

IEEE Transactions on Power Delivery ◽

10.1109/61.772319 ◽

1999 ◽

Vol 14 (3) ◽

pp. 810-817 ◽

Cited By ~ 32

Author(s):

Keng-Yu Lien ◽

Shi-Lin Chen ◽

Ching-Jung Liao ◽

Tzong-Yih Guo ◽

Tsair-Ming Lin ◽

...

Keyword(s):

Fault Detection ◽

High Impedance ◽

High Impedance Fault ◽

Threshold Tuning ◽

Tuning Scheme ◽

Variance Criterion

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Change of olfactory function as a marker of inflammatory activity and disability progression in MS

Multiple Sclerosis Journal ◽

10.1177/1352458517745724 ◽

2017 ◽

Vol 25 (2) ◽

pp. 267-274 ◽

Cited By ~ 7

Author(s):

Gabriel Bsteh ◽

Harald Hegen ◽

Felix Ladstätter ◽

Klaus Berek ◽

Matthias Amprosi ◽

...

Keyword(s):

Multivariate Model ◽

Olfactory Dysfunction ◽

Olfactory Function ◽

Odor Identification ◽

Olfactory Threshold ◽

Disability Progression ◽

Increased Risk ◽

Changes Over Time ◽

Over Time ◽

Risk Of Relapse

Background: Impaired olfactory threshold has been reported in early inflammatory phases of MS, while impaired odor identification was associated with more widespread disability. Objective: To prospectively assess the development of olfactory function and its correlation with relapse and disability progression. Methods: In this prospective, 3-year longitudinal study on 151 MS patients and 30 healthy controls, three different qualities of olfactory function (threshold, discrimination, and identification) were quantified using the Sniffin’ Sticks test. The influence of relapses and disability on olfactory function was analyzed at different time points and in a multivariate model. Results: Discrimination and identification capability significantly worsened over 3 years, while threshold did not. Threshold was markedly impaired in patients with relapse activity within 12 months, recovered in the absence of relapse, and was associated with a 2.5-fold increased risk of relapse. Deterioration of discrimination and identification was irreversible and both strongly associated with and predictive of EDSS progression. Conclusion: Olfactory function changes over time in MS. Threshold impairment is transient and predicts inflammatory disease activity, while odor identification and discrimination are associated with disability progression. Olfactory dysfunction might be a useful and easily obtainable parameter to monitor patients with regard to inflammation and neurodegeneration in MS.

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Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning

10.1101/19009423 ◽

2019 ◽

Author(s):

Hyou-Arm Joung ◽

Zachary S. Ballard ◽

Jing Wu ◽

Derek K. Tseng ◽

Hailemariam Teshome ◽

...

Keyword(s):

Lyme Disease ◽

Early Stage ◽

Point Of Care ◽

Area Under The Curve ◽

Diagnostic Algorithm ◽

Cost Effective ◽

Optimal Subset ◽

Antigen Peptide ◽

Vector Borne ◽

Threshold Tuning

ABSTRACTCaused by the tick-borne spirochete, Borrelia burgdorferi, Lyme disease (LD) is the most common vector-borne infectious disease in North America and Europe. Though timely diagnosis and treatment are effective in preventing disease progression, current tests are insensitive in early-stage LD, with a sensitivity <50%. Additionally, the serological testing currently recommended by the US Center for Disease Control has high costs (>$400/test) and extended sample-to-answer timelines (>24 hours). To address these challenges, we created a cost-effective and rapid point-of-care (POC) test for early-stage LD that assays for antibodies specific to seven Borrelia antigens and a synthetic peptide in a paper-based multiplexed vertical flow assay (xVFA). We trained a deep learning-based diagnostic algorithm to select an optimal subset of antigen/peptide targets, and then blindly-tested our xVFA using human samples (N(+) = 42, N(−)= 54), achieving an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0% respectively, outperforming previous LD POC tests. With batch-specific standardization and threshold tuning, the specificity of our blind-testing performance improved to 96.3%, with an AUC and sensitivity of 0.963 and 85.7%, respectively.

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Optimization based Layer-wise Magnitude-based Pruning for DNN Compression

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/330 ◽

2018 ◽

Cited By ~ 4

Author(s):

Guiying Li ◽

Chao Qian ◽

Chunhui Jiang ◽

Xiaofen Lu ◽

Ke Tang

Keyword(s):

Deep Neural Network ◽

Optimization Problem ◽

State Of The Art ◽

Automatic Tuning ◽

Constrained Optimization Problem ◽

Derivative Free Optimization ◽

Derivative Free ◽

Threshold Tuning ◽

Model Subject ◽

Pruning Methods

Layer-wise magnitude-based pruning (LMP) is a very popular method for deep neural network (DNN) compression. However, tuning the layer-specific thresholds is a difficult task, since the space of threshold candidates is exponentially large and the evaluation is very expensive. Previous methods are mainly by hand and require expertise. In this paper, we propose an automatic tuning approach based on optimization, named OLMP. The idea is to transform the threshold tuning problem into a constrained optimization problem (i.e., minimizing the size of the pruned model subject to a constraint on the accuracy loss), and then use powerful derivative-free optimization algorithms to solve it. To compress a trained DNN, OLMP is conducted within a new iterative pruning and adjusting pipeline. Empirical results show that OLMP can achieve the best pruning ratio on LeNet-style models (i.e., 114 times for LeNet-300-100 and 298 times for LeNet-5) compared with some state-of-the- art DNN pruning methods, and can reduce the size of an AlexNet-style network up to 82 times without accuracy loss.

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