An Integrative Approach to Infer Regulation Programs in a Transcription Regulatory Module Network

The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors. The process of learning module networks consists of two steps: first clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module, and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. Experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, achieves comparable results as given by rank aggregation

Inferring regulation programs in a transcription regulatory module network

Cells have a complex mechanism to control the expression of genes so that they are capable of adapting to environmental changes or genetic perturbations. A major part of the mechanism is fulfilled by transcription factors which can regulate the expression of other genes. Transcriptional regulatory relationships between genes and their transcription factors can be represented by a network, called a transcription regulatory network. Many algorithms have been proposed to learn transcription regulatory networks from gene expression data. In particular, the module network method, a special type of Bayesian networks, has shown promising results. In a module network, a regulatory module is a set of genes that show similar expression profiles and are regulated by a shared set of transcription factors (i.e., the regulation program of the module). This method significantly decreases the number of parameters to be learned. Module network learning consists of two tasks: clustering genes into modules and inferring the regulation program for each module. This thesis concentrates on designing algorithms for the latter task. First, we introduce a regression tree-based Gibbs sampling algorithm for learning regulation programs in module networks. The novelty of this method is that a set of tree operations is defined for generating new regression trees from a given tree. We show that the set of tree operations is sufficient to generate a well mixing Gibbs sampler even for large datasets. Second, we apply linear models to infer regulation programs. Given a gene module, this method partitions all experimental conditions into two condition clusters, between which the module's genes are most differentially expressed. Consequently, the process of learning the regulation program for the module becomes one of identifying transcription factors that are also differentially expressed between these two condition clusters. Third, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. The experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a much lower computational cost than rank aggregation, archives comparable results as given by rank aggregation

Fibre optic chemical sensor based on graphene oxide-coated long period grating

In this work, a graphene oxide-coated long period fibre grating (GO-LPG) is proposed for chemical sensing application. Graphene oxide (GO) has been deposited on the surface of long period grating to form a sensing layer which significantly enhances the interaction between LPG propagating light and the surrounding-medium. The sensing mechanism of GO-LPG relies on the change of grating resonance intensity against surrounding-medium refractive index (SRI). The proposed GO-LPG has been used to measure the concentrations of sugar aqueous solutions. The refractive index sensitivities with 99.5 dB/RIU in low refractive index region (1.33-1.35) and 320.6 dB/RIU in high index region (1.42-1.44) have been achieved, showing an enhancement by a factor of 3.2 and 6.8 for low and high index regions, respectively. The proposed GO-LPG can be further extended to the development of optical biochemical sensor with advantages of high sensitivity, real-time and label-free sensing

When Parameter-efficient Tuning Meets General-purpose Vision-language Models

Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications. However, existing works still face two main limitations: the high training costs and heavy computing resource dependence of full model fine-tuning, and the lack of semantic information in instructions, which hinders multimodal alignment. Addressing these challenges, this paper proposes a novel approach to utilize Parameter-Efficient Tuning for generAl-purpose vision-Language models, namely PETAL. PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique, which significantly reduces the training costs and reliance on heavy computing resources. Furthermore, PETAL enhances the semantic depth of instructions in two innovative ways: 1) by introducing adaptive instruction mixture-of-experts(MOEs), and 2) by fortifying the score-based linkage between parameter-efficient tuning and mutual information. Our extensive experiments across five multimodal downstream benchmarks reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness. Additionally, our approach demonstrates remarkable advantages in few-shot settings, backed by comprehensive visualization analyses. Our source code is available at: https://github. com/melonking32/PETAL

Graphene oxide functionalized long period grating for ultrasensitive label-free immunosensing

We explore graphene oxide (GO) nanosheets functionalized dual-peak long period grating (dLPG) based biosensor for ultrasensitive label-free antibody-antigen immunosensing. The GO linking layer provides a remarkable analytical platform for bioaffinity binding interface due to its favorable combination of exceptionally high surface-to-volume ratio and excellent optical and biochemical properties. A new GO deposition technique based on chemical-bonding in conjunction with physical-adsorption was proposed to offer the advantages of a strong bonding between GO and fiber device surface and a homogeneous GO overlay with desirable stability, repeatability and durability. The surface morphology of GO overlay was characterized by Atomic force microscopy, Scanning electron microscope, and Raman spectroscopy. By depositing the GO with a thickness of 49.2 nm, the sensitivity in refractive index (RI) of dLPG was increased to 2538 nm/RIU, 200% that of non-coated dLPG, in low RI region (1.333–1.347) where bioassays and biological events were usually carried out. The IgG was covalently immobilized on GO-dLPG via EDC/NHS heterobifunctional cross-linking chemistry leaving the binding sites free for target analyte recognition. The performance of immunosensing was evaluated by monitoring the kinetic bioaffinity binding between IgG and specific anti-IgG in real-time. The GO-dLPG based biosensor demonstrates an ultrahigh sensitivity with limit of detection of 7 ng/mL, which is 10-fold better than non-coated dLPG biosensor and 100-fold greater than LPG-based immunosensor. Moreover, the reusability of GO-dLPG biosensor has been facilitated by a simple regeneration procedure based on stripping off bound anti-IgG treatment. The proposed ultrasensitive biosensor can be further adapted as biophotonic platform opening up the potential for food safety, environmental monitoring, clinical diagnostics and medical applications

Prompt-Matched Semantic Segmentation

The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to various downstream tasks of semantic segmentation. Previous methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks. A few recent works attempted to insert some extra trainable parameters into the frozen networks to learn visual prompts for parameter-efficient tuning. However, these works showed poor generality as they were designed specifically for Transformers. Moreover, using limited information in these schemes, they exhibited a poor capacity to learn beneficial prompts. To alleviate these issues, we propose a novel Stage-wise Prompt-Matched Framework for generic and effective visual prompt tuning. Specifically, to ensure generality, we divide the pre-trained backbone with frozen parameters into multiple stages and perform prompt learning between different stages, which makes the proposed scheme applicable to various architectures of CNN and Transformer. For effective tuning, a lightweight Semantic-aware Prompt Matcher (SPM) is designed to progressively learn reasonable prompts with a recurrent mechanism, guided by the rich information of interim semantic maps. Working as deep matched filter of representation learning, the proposed SPM can well transform the output of the previous stage into a desirable input for the next stage, thus achieving the better matching/stimulating for the pre-trained knowledge. Extensive experiments on four benchmarks demonstrate that the proposed scheme can achieve a promising trade-off between parameter efficiency and performance effectiveness. Our code and models will be released

Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training

Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse labeled training data, most approaches either limit to specific types of text annotations or require online optimizations to cater to the texts during inference at the cost of efficiency and stability. In this paper, we investigate offline open-vocabulary text-to-motion generation in a zero-shot learning manner that neither requires paired training data nor extra online optimization to adapt for unseen texts. Inspired by the prompt learning in NLP, we pretrain a motion generator that learns to reconstruct the full motion from the masked motion. During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion. In constructing the prompt, the unmasked poses of the prompt are synthesized by a text-to-pose generator. To supervise the optimization of the text-to-pose generator, we propose the first text-pose alignment model for measuring the alignment between texts and 3D poses. And to prevent the pose generator from overfitting to limited training texts, we further propose a novel wordless training mechanism that optimizes the text-to-pose generator without any training texts. The comprehensive experimental results show that our method obtains a significant improvement against the baseline methods. The code is available at https://github.com/junfanlin/oohmg

kruX:Matrix-based non-parametric eQTL discovery

The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying genetic model and expression trait distribution, but testing billions of marker-trait combinations one-by-one can become computationally prohibitive. We developed kruX, an algorithm implemented in Matlab, Python and R that uses matrix multiplications to simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait combinations at once. KruX is more than ten thousand times faster than computing associations one-by-one on a typical human dataset. We used kruX and a dataset of more than 500k SNPs and 20k expression traits measured in 102 human blood samples to compare eQTLs detected by the Kruskal-Wallis test to eQTLs detected by the parametric ANOVA and linear model methods. We found that the Kruskal-Wallis test is more robust against data outliers and heterogeneous genotype group sizes and detects a higher proportion of non-linear associations, but is more conservative for calling additive linear associations. In summary, kruX enables the use of robust non-parametric methods for massive eQTL mapping without the need for a high-performance computing infrastructure.Comment: minor revision; 6 pages, 5 figures; software available at http://krux.googlecode.co