A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

Abstract Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.http://deepblue.lib.umich.edu/bitstream/2027.42/112478/1/12918_2013_Article_1166.pd

Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation

Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particularly in terms of syntax, has not been extensively investigated. Objective: To fill this gap, our study aims to propose a novel approach CoTR to assess and improve the syntactic adversarial robustness of PTMs in code translation. Method: CoTR consists of two components: CoTR-A and CoTR-D. CoTR-A generates adversarial examples by transforming programs, while CoTR-D proposes a semantic distance-based sampling data augmentation method and adversarial training method to improve the model's robustness and generalization capabilities. The Pass@1 metric is used by CoTR to assess the performance of PTMs, which is more suitable for code translation tasks and offers a more precise evaluation in real world scenarios. Results: The effectiveness of CoTR is evaluated through experiments on real world Java to Python datasets. The results demonstrate that CoTR-A can significantly reduce the performance of existing PTMs, while CoTR-D effectively improves the robustness of PTMs. Conclusion: Our study identifies the limitations of current PTMs, including large language models, in code translation tasks. It highlights the potential of CoTR as an effective solution to enhance the robustness of PTMs for code translation tasks.Comment: under revie

Split tolerance permits safe Ad5-GUCY2C-PADRE vaccine-induced T-cell responses in colon cancer patients.

Background: The colorectal cancer antigen GUCY2C exhibits unique split tolerance, evoking antigen-specific CD8+, but not CD4+, T-cell responses that deliver anti-tumor immunity without autoimmunity in mice. Here, the cancer vaccine Ad5-GUCY2C-PADRE was evaluated in a first-in-man phase I clinical study of patients with early-stage colorectal cancer to assess its safety and immunological efficacy. Methods: Ten patients with surgically-resected stage I or stage II (pN0) colon cancer received a single intramuscular injection of 1011 viral particles (vp) of Ad5-GUCY2C-PADRE. Safety assessment and immunomonitoring were carried out for 6 months following immunization. This trial employed continual monitoring of both efficacy and toxicity of subjects as joint primary outcomes. Results: All patients receiving Ad5-GUCY2C-PADRE completed the study and none developed adverse events greater than grade 1. Antibody responses to GUCY2C were detected in 10% of patients, while 40% exhibited GUCY2C-specific T-cell responses. GUCY2C-specific responses were exclusively CD8+ cytotoxic T cells, mimicking pre-clinical studies in mice in which GUCY2C-specific CD4+ T cells are eliminated by self-tolerance, while CD8+ T cells escape tolerance and mediate antitumor immunity. Moreover, pre-existing neutralizing antibodies (NAbs) to the Ad5 vector were associated with poor vaccine-induced responses, suggesting that Ad5 NAbs oppose GUCY2C immune responses to the vaccine in patients and supported by mouse studies. Conclusions: Split tolerance to GUCY2C in cancer patients can be exploited to safely generate antigen-specific cytotoxic CD8+, but not autoimmune CD4+, T cells by Ad5-GUCY2C-PADRE in the absence of pre-existing NAbs to the viral vector. TRIAL REGISTRATION: This trial (NCT01972737) was registered at ClinicalTrials.gov on October 30th, 2013. https://clinicaltrials.gov/ct2/show/NCT01972737

A Syntax-Guided Multi-Task Learning Approach for Turducken-Style Code Generation

Due to the development of pre-trained language models, automated code generation techniques have shown great promise in recent years. However, the generated code is difficult to meet the syntactic constraints of the target language, especially in the case of Turducken-style code, where declarative code snippets are embedded within imperative programs. In this study, we summarize the lack of syntactic constraints into three significant challenges: (1) the efficient representation of syntactic constraints, (2) the effective integration of syntactic information, and (3) the scalable syntax-first decoding algorithm. To address these challenges, we propose a syntax-guided multi-task learning approach TurduckenGen. Specifically, we first explicitly append the type information to the code tokens to capture the representation of syntactic constraints. Then we formalize code generation with syntactic constraint representation as an auxiliary task to enable the model to learn the syntactic constraints of the code. Finally, the syntactically correct code is selected accurately from the multiple candidates with the help of the compiler feedback. Extensive experiments and comprehensive analysis demonstrate the effectiveness and general applicability of our approach after being compared with six state-of-the-art baselines on two Turducken-style code datasets. Finally, we conducted a human study and found the code quality generated by our approach is better than baselines in terms of code readability and semantic similarity.Comment: Accepted in Empirical Software Engineerin

Structure-Based Investigation on the Binding and Activation of Typical Pesticides With Thyroid Receptor

A broad range of pesticides have been reported to interfere with the normal function of the thyroid endocrine system. However, the precise mechanism(s) of action has not yet been thoroughly elucidated. In this study, 21 pesticides were assessed for their binding interactions and the potential to disrupt thyroid homeostasis. In the GH3 luciferase reporter gene assays, 5 of the pesticides tested had agonistic effects in the order of procymidone &gt; imidacloprid &gt; mancozeb &gt; fluroxypyr &gt; atrazine. 11 pesticides inhibited luciferase activity of T3 to varying degrees, demonstrating their antagonistic activity. And there are 4 pesticides showed mixed effects when treated with different concentrations. Surface plasmon resonance (SPR) biosensor technique was used to directly measure the binding interactions of these pesticides to the human thyroid hormone receptor (hTR). 13 pesticides were observed to bind directly with TR, with a KD ranging from 4.80E-08 M to 9.44E-07 M. The association and disassociation of the hTR/pesticide complex revealed 2 distinctive binding modes between the agonists and antagonists. At the same time, a different binding mode was displayed by the pesticides showed mix agonist and antagonist activity. In addition, the molecular docking simulation analyses indicated that the interaction energy calculated by CDOCKER for the agonists and antagonists correlated well with the KD values measured by the surface plasmon resonance assay. These results help to explain the differences of the TR activities of these tested pesticides.</p

Diffusion-based 3D Object Detection with Random Boxes

3D object detection is an essential task for achieving autonomous driving. Existing anchor-based detection methods rely on empirical heuristics setting of anchors, which makes the algorithms lack elegance. In recent years, we have witnessed the rise of several generative models, among which diffusion models show great potential for learning the transformation of two distributions. Our proposed Diff3Det migrates the diffusion model to proposal generation for 3D object detection by considering the detection boxes as generative targets. During training, the object boxes diffuse from the ground truth boxes to the Gaussian distribution, and the decoder learns to reverse this noise process. In the inference stage, the model progressively refines a set of random boxes to the prediction results. We provide detailed experiments on the KITTI benchmark and achieve promising performance compared to classical anchor-based 3D detection methods.Comment: Accepted by PRCV 202

Design and Mechanical Compatibility of Nylon Bionic Cancellous Bone Fabricated by Selective Laser Sintering

In order to avoid the stress shielding phenomenon in orthopedic bionic bone implantation, it is necessary to consider the design of mechanical compatible implants imitating the host bone. In this study, we developed a novel cancellous bone structure design method aimed at ensuring the mechanical compatibility between the bionic bone and human bone by means of computer-aided design (CAD) and finite element analysis technology (specifically, finite element modeling (FEM)). An orthogonal lattice model with volume porosity between 59% and 96% was developed by means of CAD. The effective equivalent elastic modulus of a honeycomb structure with square holes was studied by FEM simulation. With the purpose of verifying the validity of the cancellous bone structure design method, the honeycomb structure was fabricated by selective laser sintering (SLS) and the actual equivalent elastic modulus of the honeycomb structure was measured with a uniaxial compression test. The experimental results were compared with the FEM values and the predicted values. The results showed that the stiffness values of the designed structures were within the acceptable range of human cancellous bone of 50-500 MPa, which was similar to the stiffness values of human vertebrae L1 and L5. From the point of view of mechanical strength, the established cellular model can effectively match the elastic modulus of human vertebrae cancellous bone. The functional relationship between the volume porosity of the nylon square-pore honeycomb structure ranging from 59% to 96% and the effective elastic modulus was established. The effect of structural changes related to the manufacture of honeycomb structures on the equivalent elastic modulus of honeycomb structures was studied quantitatively by finite element modeling

High-Speed Serial Optical Link Test Bench Using FPGA with Embedded Transceivers

We develop a custom Bit Error Rate test bench based on Altera’s Stratix II GX transceiver signal integrity development kit, demonstrate it on point-to-point serial optical link with data rate up to 5 Gbps, and compare it with commercial stand alone tester. The 8B/10B protocol is implemented and its effects studied. A variable optical attenuator is inserted in the fibre loop to induce transmission degradation and to measure receiver sensitivity. We report comparable receiver sensitivity results using the FPGA based tester and commercial tester. The results of the FPGA also shows that there are more one-tozero bit flips than zero-to-one bit flips at lower error rate. In 8B/10B coded transmission, there are more word errors than bit flips, and the total error rate is less than two times that of non-coded transmission. Total error rate measured complies with simulation results, according to the protocol setup