Diffusion-based Molecule Generation with Informative Prior Bridges

AI-based molecule generation provides a promising approach to a large area of biomedical sciences and engineering, such as antibody design, hydrolase engineering, or vaccine development. Because the molecules are governed by physical laws, a key challenge is to incorporate prior information into the training procedure to generate high-quality and realistic molecules. We propose a simple and novel approach to steer the training of diffusion-based generative models with physical and statistics prior information. This is achieved by constructing physically informed diffusion bridges, stochastic processes that guarantee to yield a given observation at the fixed terminal time. We develop a Lyapunov function based method to construct and determine bridges, and propose a number of proposals of informative prior bridges for both high-quality molecule generation and uniformity-promoted 3D point cloud generation. With comprehensive experiments, we show that our method provides a powerful approach to the 3D generation task, yielding molecule structures with better quality and stability scores and more uniformly distributed point clouds of high qualities

π\pi-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation

Foundation models have achieved great advances in multi-task learning with a unified interface of unimodal and multimodal tasks. However, the potential of such multi-task learners has not been exploited during transfer learning. In this work, we present a universal parameter-efficient transfer learning method, termed Predict-Interpolate Tuning ($\pi$-Tuning), for vision, language, and vision-language tasks. It aggregates the parameters of lightweight task-specific experts learned from similar tasks to aid the target downstream task. The task similarities are predicted in a unified modality-independent space, yielding a scalable graph to demonstrate task relationships. $\pi$-Tuning has several appealing benefits. First, it flexibly explores both intra- and inter-modal transferability between similar tasks to improve the accuracy and robustness of transfer learning, especially in data-scarce scenarios. Second, it offers a systematical solution for transfer learning with multi-task prediction-and-then-interpolation, compatible with diverse types of parameter-efficient experts, such as prompt and adapter. Third, an extensive study of task-level mutual benefits on 14 unimodal and 6 multimodal datasets shows that $\pi$-Tuning surpasses fine-tuning and other parameter-efficient transfer learning methods both in full-shot and low-shot regimes. The task graph also enables an in-depth interpretable analysis of task transferability across modalities.Comment: To appear in ICML 202

Concurrent administration of amiodarone and atenolol in the treatment of coronary artery disease complicated with arrhythmia, and its effect on serum levels of CD40L, TNF-α and IL-6

Purpose: To investigate the efficacy of the combination of amiodarone and atenolol in the treatment of patients with coronary artery disease (CAD) complicated with arrhythmia, and its effect on serum levels of CD-40L, TNF-α and IL-6.Methods: One hundred and twenty CAD patients with arrhythmia on admission in The First People'sHospital of Shuangliu District Chengdu, China were assigned to groups A and B, each having 60 patients. Amiodarone was administered to all the patients, while atenolol was additionally given to patients in group A. Levels of heart function indicators, inflammatory factors, blood pressure, heart rate, adverse reaction rate (ARR) and overall efficacy were evaluated for the two groups.Results: There were significantly improved levels of heart function indicators, and lower levels of CD40L, TNF-α and IL-6 in group A, when compared with group B (p < 0.001). Moreover, treatment effectiveness was higher in group A than in group B (p < 0.05). However, there was no significant difference (p > 0.05) in ARR between groups A and B.Conclusion: The combined use of amiodarone and atenolol improves heart function indicators in patients with CAD and arrhythmia, reduces the levels of inflammatory factors, normalizes blood pressure and heart rate, and lowers ARR. However, further clinical trials on this combined therapy are required prior to its use in clinical practice

Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images

Photos serve as a way for humans to record what they experience in their daily lives, and they are often regarded as trustworthy sources of information. However, there is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos, which can create confusion and diminish trust in photographs. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content. Our study benchmarks both human capability and cutting-edge fake image detection AI algorithms, using a newly collected large-scale fake image dataset Fake2M. In our human perception evaluation, titled HPBench, we discovered that humans struggle significantly to distinguish real photos from AI-generated ones, with a misclassification rate of 38.7%. Along with this, we conduct the model capability of AI-Generated images detection evaluation MPBench and the top-performing model from MPBench achieves a 13% failure rate under the same setting used in the human evaluation. We hope that our study can raise awareness of the potential risks of AI-generated images and facilitate further research to prevent the spread of false information. More information can refer to https://github.com/Inf-imagine/Sentry

Effects of a Rehabilitation Program for Individuals with Chronic Spinal Cord Injury in Shanghai, China

Background: Specialized Institution-Based Rehabilitation (SIBR) is the cornerstone of care and treatment for individuals with spinal cord injury, but most people with chronic spinal cord injury (CSCI) living in China have no SIBR experience after acute care hospital discharge. In 2009, an SIBR facility was set up in Shanghai (China) to fill this important gap in care. The purpose of the study was to evaluate the effectiveness of an integrated rehabilitation training program among individuals with CSCI living in Shanghai. Methods: A within-subject pre-posttest design was used to evaluate the SIBR. The sample included 455 individuals ≥1 year post-SCI, who were older than 18 years of age and were enrolled in a rehabilitation center in Shanghai, China, between 2013 and 2019. The data included individuals’ sociodemographic and injury characteristics, and twenty-three indicators were used as outcome measurements to evaluate basic life skills and their applications in family and social life. Multivariate linear regression was conducted to determine which factors might have influenced the effectiveness of the SIBR. Results: All basic life skills and their applications in family and social life were improved, but with variations across socio-demographics. Female individuals with CSCI had better outcomes in basic life skills than did males. In terms of basic life skills and their applications in family and social life, individuals with a low level (thoracic or lumbosacral) of injury achieved more significant functional gains than those with a higher level (cervical). The baseline score was also a relevant factor in functional outcome. Conclusions: Even for individuals with a long SCI history, SIBR training can improve basic life skills and the applications of those skills in family and social life settings

An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data

The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1). Final images from CD+r training were evaluated at the \emph{argmin} of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized root-mean square error, the concordance correlation coefficient (CCC), and the structural similarity index (SSIM). The CD+r reconstructions, chosen using the stopping condition, yielded SSIMs that were similar to the CD (p=0.47) and LR SSIMs (p=0.95) across R and that were significantly higher than the L1 SSIMs (p=0.04). The CCC values for the CD+r T1 maps across all R and subjects were greater than those corresponding to the L1 (p=0.15) and LR (p=0.13) T1 maps, respectively. For R > 12 (<4.2 minutes scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD+r. We conclude that the use of an untrained neural network together with a physics-based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully-sampled ground truth data.Comment: 45 pages, 7 figures, 2 Tables, supplementary material included (10 figures, 4 tables

Simulation of the Particle Transport Behaviors in Nanoporous Matter

The transport behaviors of proton into nanoporous materials were investigated using different Monte Carlo simulation codes such as GEANT4, Deeper and SRIM. The results indicated that porous structure could enhance the proton scattering effects due to a higher specific surface area and more boundaries. The existence of voids can deepen and widen the proton distribution in the targets due to relatively lower apparent density. Thus, the incident protons would transport deeper and form a wider Bragg peak in the end of the range, as the target materials are in a higher porosity state and/or have a larger pore size. The existence of voids also causes the local inhomogeneity of proton/energy distribution in micro/nano scales. As compared, the commonly used SRIM code can only be used to estimate roughly the incident proton range in nanoporous materials, based on a homogeneous apparent density equivalence rule. Moreover, the estimated errors of the proton range tend to increase with the porosity. The Deeper code (designed for evaluation of radiation effects of nuclear materials) can be used to simulate the transport behaviors of protons or heavy ions in a real porous material with porosity smaller than 52.3% due to its modeling difficulty, while the GEANT4 code has shown advantages in that it is suitable and has been proven to simulate proton transportation in nanoporous materials with porosity in its full range of 0~100%. The GEANT4 simulation results are proved consistent with the experimental data, implying compatibility to deal with ion transportation into homogeneously nanoporous materials

Removal of foreign bodies embedded in the urinary bladder wall by a combination of laparoscopy and carbon dioxide cystoscopic assistance: Case report and literature review

Purpose: To report a case of combined laparoscopic and carbon dioxide partial cystectomy and foreign body removal and to review the existing literature on the topic. Materials and Methods: A 43-year-old Asian woman was found to have an intrauterine device embedded in the bladder wall during evaluation for chronic pelvic pain and urinary tract infection. She underwent cystoscopic-laparoscopic partial cystectomy, with an uncomplicated postoperative course. She had normal renal function during the follow-up period. This case demonstrates the possibility and safety of performing cystoscopic-laparoscopic partial cystectomy for the removal of a partially implanted intravesical foreign body. Results: The patient recovered without incident and was discharged 7 days after surgery. No abnormalities were noted in the urine output or renal function in the postoperative follow-up period. No complications due to retrograde flow of carbon dioxide up the ureters or air embolism were noted during the procedure or postoperatively. Conclusions: The combination of laparoscopy and air cystoscopy has been shown to be an optimal method for retracting foreign bodies embedded in the bladder wall. Also, air cystoscopy can be used to give doctors a better view in cases in which vision is compromised under water-contrast cystoscopy