Confounder balancing in adversarial domain adaptation for pre-trained large models fine-tuning

The excellent generalization, contextual learning, and emergence abilities in the pre-trained large models (PLMs) handle specific tasks without direct training data, making them the better foundation models in the adversarial domain adaptation (ADA) methods to transfer knowledge learned from the source domain to target domains. However, existing ADA methods fail to account for the confounder properly, which is the root cause of the source data distribution that differs from the target domains. This study proposes a confounder balancing method in adversarial domain adaptation for PLMs fine-tuning (CadaFT), which includes a PLM as the foundation model for a feature extractor, a domain classifier and a confounder classifier, and they are jointly trained with an adversarial loss. This loss is designed to improve the domain-invariant representation learning by diluting the discrimination in the domain classifier. At the same time, the adversarial loss also balances the confounder distribution among source and unmeasured domains in training. Compared to newest ADA methods, CadaFT can correctly identify confounders in domain-invariant features, thereby eliminating the confounder biases in the extracted features from PLMs. The confounder classifier in CadaFT is designed as a plug-and-play and can be applied in the confounder measurable, unmeasurable, or partially measurable environments. Empirical results on natural language processing and computer vision downstream tasks show that CadaFT outperforms the newest GPT-4, LLaMA2, ViT and ADA methods.

A handheld plug-and-play microfluidic liquid handling automation platform for immunoassays

Lab-on-a-chip technologies and microfluidics have pushed miniaturized liquid handling to unprecedented precision, integration, and automation, which improved the reaction efficiency of immunoassays. However, most microfluidic immunoassay systems still require bulky infrastructures, such as external pressure sources, pneumatic systems, and complex manual tubing and interface connections. Such requirements prevent plug-and-play operation at the point-of-care (POC) settings. Here we present a fully automated handheld general microfluidic liquid handling automation platform with a plug-and-play 'clamshell-style' cartridge socket, a miniature electro-pneumatic controller, and injection-moldable plastic cartridges. The system achieved multi-reagent switching, metering, and timing control on the valveless cartridge using electro-pneumatic pressure control. As a demonstration, a SARS-CoV-2 spike antibody sandwich fluorescent immunoassay (FIA) liquid handling was performed on an acrylic cartridge without human intervention after sample introduction. A fluorescence microscope was used to analyze the result. The assay showed a limit of detection at 31.1 ng/mL, comparable to some previously reported enzyme-linked immunosorbent assays (ELISA). In addition to automated liquid handling on the cartridge, the system can operate as a 6-port pressure source for external microfluidic chips. A rechargeable battery with a 12 V 3000 mAh capacity can power the system for 42 h. The footprint of the system is 16.5 × 10.5 × 7 cm, and the weight is 801 g, including the battery. The system can find many other POC and research applications requiring complex liquid manipulation, such as molecular diagnostics, cell analysis, and on-demand biomanufacturing.

Synchrotron microtomography image restoration via regularization representation and deep CNN prior

BACKGROUND AND OBJECTIVE

Synchrotron-based X-ray microtomography (S-µCT) is a promising imaging technique that plays an important role in modern medical science. S-µCT systems often cause various artifacts and noises in the reconstructed CT images, such as ring artifacts, quantum noise, and electronic noise. In most situations, such noise and artifacts occur simultaneously, which results in a deterioration in the image quality and affects subsequent research. Due to the complexity of the distribution of these mixed artifacts and noise, it is difficult to restore the corrupted images. To address this issue, we propose a novel algorithm to remove mixed artifacts and noise from S-µCT images simultaneously.

METHODS

There are two important aspects of our method. Regarding ring artifacts, because of their specific structural characteristics, regularization-based methods are more suitable; thus, low-rank tensor decomposition and total variation are utilized to represent their directional and locally piecewise smoothness properties. Moreover, to determine the implicit prior of the random noise, a convolutional neural network (CNN) based method is used. The advantages of traditional regularization and the deep CNN are then combined and embedded in a plug-and-play framework. Hence, an efficient image restoration algorithm is proposed to address the problem of mixed artifacts and noise in S-µCT images.

RESULTS

Our proposed method was assessed by utilizing simulations and real data experiments. The qualitative results showed that the proposed method could effectively remove ring artifacts as well as random noise. The quantitative results demonstrated that the proposed method achieved almost the best results in terms of PSNR, SSIM and MAE compared to other methods.

CONCLUSIONS

The proposed method can serve as an effective tool for restoring corrupted S-µCT images, and it has the potential to promote the application of S-µCT.