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Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
This study investigated how touching and being touched by a humanoid robot affects human physiology, impressions of the interaction, and attitudes towards humanoid robots. 21 healthy adult participants completed a 3 (touch style: touching, being touched, pointing) x 2 (body part: hand vs buttock) within-subject design using a Pepper robot. Skin conductance response (SCR) was measured during each interaction. Perceived impressions of the interaction (i.e., friendliness, comfort, arousal) were measured per questionnaire after each interaction. Participants' demographics and their attitude towards robots were also considered. We found shorter SCR rise times in the being touched compared to the touching condition, possibly reflecting psychological alertness to the unpredictability of robot-initiated contacts. The hand condition had shorter rise times than the buttock condition. Most participants evaluated the hand condition as most friendly and comfortable and the robot-initiated interactions as most arousing. Interacting with Pepper improved attitudes towards robots. Our findings require future studies with larger samples and improved procedures. They have implications for robot design in all domains involving tactile interactions, such as caring and intimacy.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
How We Found Our IMU
(2020)
Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
We present an electrochemical MIP sensor for tamoxifen (TAM)-a nonsteroidal anti-estrogen-which is based on the electropolymerisation of an O-phenylenediamine. resorcinol mixture directly on the electrode surface in the presence of the template molecule. Up to now only. bulk. MIPs for TAM have been described in literature, which are applied for separation in chromatography columns. Electro-polymerisation of the monomers in the presence of TAM generated a film which completely suppressed the reduction of ferricyanide. Removal of the template gave a markedly increased ferricyanide signal, which was again suppressed after rebinding as expected for filling of the cavities by target binding. The decrease of the ferricyanide peak of the MIP electrode depended linearly on the TAM concentration between 1 and 100 nM. The TAM-imprinted electrode showed a 2.3 times higher recognition of the template molecule itself as compared to its metabolite 4-hydroxytamoxifen and no cross-reactivity with the anticancer drug doxorubucin was found. Measurements at + 1.1 V caused a fouling of the electrode surface, whilst pretreatment of TAM with peroxide in presence of HRP generated an oxidation product which was reducible at 0 mV, thus circumventing the polymer formation and electrochemical interferences.
Simple and robust
(2021)
A spectrum of 7562 publications on Molecularly Imprinted Polymers (MIPs) has been presented in literature within the last ten years (Scopus, September 7, 2020). Around 10 % of the papers published on MIPs describe the recognition of proteins. The straightforward synthesis of MIPs is a significant advantage as compared with the preparation of enzymes or antibodies. MIPs have been synthesized from only one up to six functional monomers while proteins are made up of 20 natural amino acids. Furthermore, they can be synthesized against structures of low immunogenicity and allow multi-analyte measurements via multi-target synthesis. Electrochemical methods allow simple polymer synthesis, removal of the template and readout. Among the different sensor configurations electrochemical MIP-sensors provide the broadest spectrum of protein analytes. The sensitivity of MIP-sensors is sufficiently high for biomarkers in the sub-nanomolar region, nevertheless the cross-reactivity of highly abundant proteins in human serum is still a challenge. MIPs for proteins offer innovative tools not only for clinical and environmental analysis, but also for bioimaging, therapy and protein engineering.
The goal of this three-year longitudinal study was to examine the buffering effect of parental mediation of adolescents’ technology use (i.e., restrictive, co-viewing, and instructive) on the relationships among cyber aggression involvement and substance use (i.e., alcohol use, marijuana use, cigarette smoking, and non-marijuana illicit drug use). Overall, 867 (M age = 13.67, age range from 13–15 years, 51% female, 49% White) 8th grade adolescents from the Midwestern United States participated in this study during the 6th grade (Wave 1), 7th grade (Wave 2), and 8th grade (Wave 3). Results revealed that higher levels of Wave 2 instructive mediation weakened the association between Wave 1 cyber victimization and Wave 3 alcohol use and Wave 3 non-marijuana illicit drug use. The relationship was stronger between Wave 1 cyber victimization and Wave 3 alcohol use and Wave 3 non-marijuana illicit drug use when adolescents reported lower levels of Wave 2 instructive mediation. At lower levels of Wave 2 instructive mediation, the association between Wave 1 cyber aggression perpetration and Wave 3 non-marijuana illicit drug use was stronger. Implications of these findings are discussed in the context of parents recognizing their role in helping to mitigate the negative consequences associated with adolescents’ cyber aggression involvement.