Adversarial Patch Attacks on Monocular Depth Estimation Networks

Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years. However, depth estimation from a monocular image alone is essentially an ill-posed problem, and thus, it seems that this approach would have inherent vulnerabilities. To reveal this limitation, we propose a method of adversarial patch attack on monocular depth estimation. More specifically, we generate artificial patterns (adversarial patches) that can fool the target methods into estimating an incorrect depth for the regions where the patterns are placed. Our method can be implemented in the real world by physically placing the printed patterns in real scenes. We also analyze the behavior of monocular depth estimation under attacks by visualizing the activation levels of the intermediate layers and the regions potentially affected by the adversarial attack.Comment: Publisher's Open Access PDF with the CC-BY copyright. Associated video, data and programs are available at https://www.fujii.nuee.nagoya-u.ac.jp/Research/MonoDepth

Identification of Respiratory Sounds Collected from Microphones Embedded in Mobile Phones

Sudden deterioration of condition in patients with various diseases, such as cardiopulmonary arrest, may result in poor outcome even after resuscitation. Early detection of deterioration is important in medical and long-term care settings, regardless of the acute or chronic phase of disease. Early detection and appropriate interventions are essential before resuscitating measures are required. Among the vital signs that indicate the general condition of a patient, respiratory rate has a greater ability to predict serious events such as thromboembolism and sepsis than heart rate and blood pressure, even in early stages. Despite its importance, however, respiratory rate is frequently overlooked and not measured, making it a neglected vital sign. To facilitate the measurement of respiratory rate, a non-invasive method of detecting respiratory sounds was developed based on deep learning technology, using a built-in microphone in a smartphone. Smartphones attached to the bed headboards of 20 participants undergoing polysomnography (PSG) at Kyoto University Hospital recorded respiratory sounds. Sound data were synchronized with overnight respiratory information. After excluding periods of abnormal breathing on the PSG report, sound data were processed for each 1-minute period. Expiration sound was determined using the pressure flow sensor signal on PSG. Finally, a model to identify the expiration section from the sound information was created using a deep learning algorithm from the convolutional Long Short Term Memory network. The accuracy of the learning model in identifying the expiratory section was 0.791, indicating that respiratory rate can be determined using the microphone in a smartphone. By collecting data from more patients and improving the accuracy of this method, respiratory rates could be more easily monitored in all situations, both inside and outside the hospital

The pathophysiology of prospective memory failure after diffuse axonal injury - Lesion-symptom analysis using diffusion tensor imaging

<p>Abstract</p> <p>Background</p> <p>Prospective memory (PM) is one of the most important cognitive domains in everyday life. The neuronal basis of PM has been examined by a large number of neuroimaging and neuropsychological studies, and it has been suggested that several cerebral domains contribute to PM. For these activation studies, a constellation of experimental PM trials was developed and adopted to healthy subjects. In the present study, we used a widely used clinical PM assessment battery to determine the lesions attributable to PM failure, with the hypothesis that lesion-symptom analysis using diffusion tensor imaging (DTI) in subjects with diffuse axonal injury (DAI) can reveal the neuronal basis of PM in everyday life.</p> <p>Results</p> <p>Fourteen DAI patients (age: range of 18-36, median 24) participated in this study. PM failure was scored in the range of 0-6 using three sub-tests of the Rivermead Behavioural Memory Test. The PM scores of DAI patients were in the range of 2-6 (median 4.5, inter-quartile range 2.25). The severity of axonal injury following DAI was examined using fractional anisotropy (FA), one of the DTI parameters, at voxel level in each subject. We then obtained clusters correlated with PM failure by conducting voxel-based regression analysis between FA values and PM scores. Three clusters exhibited significant positive correlation with PM score, the left parahippocampal gyrus, left inferior parietal lobe, and left anterior cingulate.</p> <p>Conclusions</p> <p>This is the first lesion-symptom study to reveal the neuronal basis of PM using DTI on subjects with DAI. Our findings suggest that the neuronal basis of PM is in the left parahippocampal gyrus, left inferior parietal lobe, and/or left anterior cingulate. These findings are similar to those of previous activation studies with loading experimental PM tasks.</p

Inter-assay variability of next-generation sequencing-based gene panels

BACKGROUND: Tumor heterogeneity has been known to cause inter-assay discordance among next-generation sequencing (NGS) results. However, whether preclinical factors such as sample type, sample quality and analytical features of gene panel can affect the concordance between two different assays remains largely unexplored. METHODS: Replicate sets of DNA samples extracted from formalin-fixed paraffin-embedded tissues (FFPE) (n = 20) and fresh frozen (FF) tissues (n = 10) were herein analyzed using a tumor-only (TO) and paired tumor-normal (TN) gene panel in laboratories certified by the Clinical Laboratory Improvement Amendment. Reported variants from the TO and TN panels were then compared. Furthermore, additional FFPE samples were sequentially sliced from the same FFPE block and submitted to another TN panel assay. RESULTS: Substantial discordance (71.8%) was observed between the results of the two panels despite using identical DNA samples, with the discordance rate being significantly higher for FFPE samples (p < 0.05). Among the 99 variants reported only in the TO panel, 32.3% were consistent with germline variants, which were excluded in the TN panel, while 30.3% had an allele frequency of less than 5%, some of which were highly likely to be artificial calls. The comparison of two independent TN panel assay results from the same FFPE block also showed substantial discordance rate (55.3%). CONCLUSIONS: In the context of clinical settings, our comparative analysis revealed that inter-NGS assay discordance commonly occurred due to sample types and the different analytical features of each panel

Early expression of serum CCL8 closely correlates to non-relapse mortality after allogeneic hematopoietic stem cell transplantation

To explore the role of Chemokine (C-C motif) ligand 8 (CCL8) as a potential biomarker for acute graft-versus-host disease (aGVHD), we retrospectively analyzed the sera and clinical course of 31 patients with grade II?IV aGVHD. No deaths occurred in the ten patients with serum CCL8 concentrations less than 213 pg/mL, whereas 11 of the 21 patients with more than 213 pg/mL died within 180 days post-transplantation. This landmark analysis revealed a significantly lower urvival rate of patients with a CCL8 serum concentration greater than 213 pg/mL. Thus, elevated serum CCL8 concentration before day 100 post-transplantation may predict aGVHD prognosi

Prevalence of pathogenic germline variants in the circulating tumor DNA testing

BACKGROUND: Somatic and germline variants are not distinguishable by circulating tumor DNA (ctDNA) testing without analyzing non-tumor samples. Although confirmatory germline testing is clinically relevant, the criteria for selecting presumed germline variants have not been established in ctDNA testing. In the present study, we aimed to evaluate the prevalence of pathogenic germline variants in clinical ctDNA testing through their variant allele fractions (VAFs). METHODS: A total of consecutive 106 patients with advanced solid tumors who underwent ctDNA testing (Guardant360®) between January 2018 and March 2020 were eligible for this study. To verify the origin of pathogenic variants reported in ctDNA testing, germline sequencing was performed using peripheral blood DNA samples archived in the Clinical Bioresource Center in Kyoto University Hospital (Kyoto, Japan) under clinical research settings. RESULTS: Among 223 pathogenic variants reported in ctDNA testing, the median VAF was 0.9% (0.02-81.8%), and 88 variants with ≥ 1% VAFs were analyzed in germline sequencing. Among 25 variants with ≥ 30% VAFs, seven were found in peripheral blood DNA (BRCA2: n = 6, JAK2: n = 1). In contrast, among the 63 variants with VAFs ranging from 1 to < 30%, only one variant was found in peripheral blood DNA (TP53: n = 1). Eventually, this variant with 15.6% VAF was defined to be an acquired variant, because its allelic distribution did not completely link to those of neighboring germline polymorphisms. CONCLUSION: Our current study demonstrated that VAFs values are helpful for selecting presumed germline variants in clinical ctDNA testing