BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention. While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped. However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility. Besides, it is hard to meet a large memory requirement for modern PTMs. In this work, we propose black-box visual prompting (BlackVIP), which efficiently adapts the PTMs without knowledge about model architectures and parameters. BlackVIP has two components; 1) Coordinator and 2) simultaneous perturbation stochastic approximation with gradient correction (SPSA-GC). The Coordinator designs input-dependent image-shaped visual prompts, which improves few-shot adaptation and robustness on distribution/location shift. SPSA-GC efficiently estimates the gradient of a target model to update Coordinator. Extensive experiments on 16 datasets demonstrate that BlackVIP enables robust adaptation to diverse domains without accessing PTMs' parameters, with minimal memory requirements. Code: \url{https://github.com/changdaeoh/BlackVIP}Comment: Accepted to CVPR 202

RNA sequencing analysis of hepatocellular carcinoma identified oxidative phosphorylation as a major pathologic feature

Abstract Dysregulation of expression of functional genes and pathways plays critical roles in the etiology and progression of hepatocellular carcinoma (HCC). Next generation‐based RNA sequencing (RNA‐seq) offers unparalleled power to comprehensively characterize HCC at the whole transcriptome level. In this study, 17 fresh‐frozen HCC samples with paired non‐neoplastic liver tissue from Caucasian patients undergoing liver resection or transplantation were used for RNA‐seq analysis. Pairwise differential expression analysis of the RNA‐seq data was performed to identify genes, pathways, and functional terms differentially regulated in HCC versus normal tissues. At a false discovery rate (FDR) of 0.10, 13% (n = 4335) of transcripts were up‐regulated and 19% (n = 6454) of transcripts were down‐regulated in HCC versus non‐neoplastic tissue. Eighty‐five Kyoto Encyclopedia of Genes and Genomes pathways were differentially regulated (FDR, <0.10), with almost all pathways (n = 83) being up‐regulated in HCC versus non‐neoplastic tissue. Among the top up‐regulated pathways was oxidative phosphorylation (hsa00190; FDR, 1.12E‐15), which was confirmed by Database for Annotation, Visualization, and Integrated Discovery (DAVID) gene set enrichment analysis. Consistent with potential oxidative stress due to activated oxidative phosphorylation, DNA damage‐related signals (e.g., the up‐regulated hsa03420 nucleotide excision repair [FDR, 1.14E‐04] and hsa03410 base excision repair [FDR, 2.71E‐04] pathways) were observed. Among down‐regulated genes (FDR, <0.10), functional terms related to cellular structures (e.g., cell membrane [FDR, 3.05E‐21] and cell junction [FDR, 2.41E‐07], were highly enriched, suggesting compromised formation of cellular structure in HCC at the transcriptome level. Interestingly, the olfactory transduction (hsa04740; FDR, 1.53E‐07) pathway was observed to be down‐regulated in HCC versus non‐neoplastic tissue, suggesting impaired liver chemosensory functions in HCC. Our findings suggest oxidative phosphorylation and the associated DNA damage may be the major driving pathologic feature in HCC

Widefield Scanning Wavefront Sensor (WSWS)

This video shows the multi-directional peripheral scanning capability of our novel Widefield Scanning Wavefront Sensor (WSWS). The device can scan along any retinal meridian by using a unique mechanism that involves the concurrent operation of a motorized rotary stage and a goniometer. We tested scanning along four meridians, which involves 60° horizontal scan (±30° from the fovea), 36° vertical scan and two 36° diagonal scans (±18° from the fovea), each completed within a time frame of 5 seconds

Synthesis of Stimuli-Responsive, Deep Eutectic Solvent-Based Polymer Thermosets for Debondable Adhesives

This paper demonstrates a macromolecular design for deep eutectic solvent (DES)-based polymer thermosets that are adhesive but removable on demand by depolymerization. For the design of a DES, a novel self-immolative polymerizable molecule capable of donating hydrogen bonds has been synthesized to form a room-temperature eutectic mixture when combined with another olefinic hydrogen bond acceptor. The physical properties of the liquid mixture have been characterized, and the mixture has been confirmed to be suitable for the formation of easily processable, resilient, transparent thermosets through click addition polymerization. The materials not only degrade on a molecular level as designed but also show interfacial adhesion onto various substrates, yielding a debondable polymer adhesive. The adhesive strength, which is comparable to that of commercial glue, decreases significantly in response to trace amounts of fluoride under benign conditions. As an example, after exposure to 0.01 M CsF, the bonded glass substrates easily separated within 16 h at room temperature. Similarly, the energy-efficient delamination of mixed composites was also achieved. We envisage that our design concept would benefit the development of functional polymeric materials that facilitate end-of-use processes

Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors

Abstract Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR <90 mL/min/1.73 m2 or proteinuria at baseline. In the UK Biobank, 720/30,477 (2.4%) participants had CKD events during the 10.8-year follow-up period. In the Korean Diabetic Cohort, 206/5014 (4.1%) had CKD events during the 6.1-year follow-up period. When the validation cohorts were divided into quartiles of Reti-CKD score, the hazard ratios for CKD development were 3.68 (95% Confidence Interval [CI], 2.88–4.41) in the UK Biobank and 9.36 (5.26–16.67) in the Korean Diabetic Cohort in the highest quartile compared to the lowest. The Reti-CKD score, compared to eGFR based methods, showed a superior concordance index for predicting CKD incidence, with a delta of 0.020 (95% CI, 0.011–0.029) in the UK Biobank and 0.024 (95% CI, 0.002–0.046) in the Korean Diabetic Cohort. In people with preserved kidney function, the Reti-CKD score effectively stratifies future CKD risk with greater performance than conventional eGFR-based methods

Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms

Background The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for predicting other systemic biomarkers and the generalisability of this approach to various populations remains unexplored. Methods With use of 236 257 retinal photographs from seven diverse Asian and European cohorts (two health screening centres in South Korea, the Beijing Eye Study, three cohorts in the Singapore Epidemiology of Eye Diseases study, and the UK Biobank), we evaluated the capacities of 47 deep-learning algorithms to predict 47 systemic biomarkers as outcome variables, including demographic factors (age and sex); body composition measurements; blood pressure; haematological parameters; lipid profiles; biochemical measures; biomarkers related to liver function, thyroid function, kidney function, and inflammation; and diabetes. The standard neural network architecture of VGG16 was adopted for model development. Findings In addition to previously reported systemic biomarkers, we showed quantification of body composition indices (muscle mass, height, and bodyweight) and creatinine from retinal photographs. Body muscle mass could be predicted with an R-2 of 0.52 (95% CI 0.51-0.53) in the internal test set, and of 0.33 (0.30-0.35) in one external test set with muscle mass measurement available. The R-2 value for the prediction of height was 0.42 (0.40-0.43), of bodyweight was 0.36 (0.34-0.37), and of creatinine was 0.38 (0.37-0.40) in the internal test set. However, the performances were poorer in external test sets (with the lowest performance in the European cohort), with R-2 values ranging between 0.08 and 0.28 for height, 0.04 and 0.19 for bodyweight, and 0.01 and 0.26 for creatinine. Of the 47 systemic biomarkers, 37 could not be predicted well from retinal photographs via deep learning (R-2=0.14 across all external test sets). Interpretation Our work provides new insights into the potential use of retinal photographs to predict systemic biomarkers, including body composition indices and serum creatinine, using deep learning in populations with a similar ethnic background. Further evaluations are warranted to validate these findings and evaluate the clinical utility of these algorithms.11Nsciescopu