An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction

White-box Compiler Fuzzing Empowered by Large Language Models

Compiler correctness is crucial, as miscompilation falsifying the program behaviors can lead to serious consequences. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates tests without sufficient understanding of internal compiler behaviors. As such, they often fail to construct programs to exercise conditions of intricate optimizations. Meanwhile, traditional white-box techniques are computationally inapplicable to the giant codebase of compilers. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, prompting LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization. WhiteFox adopts a dual-model framework: (i) an analysis LLM examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) a generation LLM produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are used as feedback to further enhance the test generation on the fly. Our evaluation on four popular compilers shows that WhiteFox can generate high-quality tests to exercise deep optimizations requiring intricate conditions, practicing up to 80 more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 96 bugs, with 80 confirmed as previously unknown and 51 already fixed. Beyond compiler testing, WhiteFox can also be adapted for white-box fuzzing of other complex, real-world software systems in general

Meta-Aerogels: Auxetic Shape-Memory Polyurethane Aerogels

Shape-memory poly(isocyanurate-urethane) (PIR-PUR) aerogels are low-density monolithic nanoporous solids that remember and return to their permanent shape through a heating actuation step. Herein, through structural design at the macro scale, the shape-memory response is augmented with an auxetic effect manifested by a negative Poisson\u27s ratio of approximately -0.8 at 15% compressive strain. Thus, auxetic shape-memory PIR-PUR monoliths experience volume contraction upon compression at a temperature above the glass transition temperature of the base polymer (Tg ≈ 30 °C), and they can be stowed indefinitely in that temporary shape by cooling below Tg. By heating back above Tg, the compressed/shrunk form expands back to their original shape/size. This technology is relevant to a broad range of industries spanning the commercial, aeronautical, and aerospace sectors. The materials are referred to as meta-aerogels, and their potential applications include minimally invasive medical devices, soft robotics, and situations where volume is at a premium, as for example for storage of deployable space structures and planetary habitats during transport to the point of service

Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization of 110 tumors and 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, and acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, and gender. Proteomic and phosphoproteomic data illuminated biology downstream of copy number aberrations, somatic mutations, and fusions and identified therapeutic vulnerabilities associated with driver events involving KRAS, EGFR, and ALK. Immune subtyping revealed a complex landscape, reinforced the association of STK11 with immune-cold behavior, and underscored a potential immunosuppressive role of neutrophil degranulation. Smoking-associated LUADs showed correlation with other environmental exposure signatures and a field effect in NATs. Matched NATs allowed identification of differentially expressed proteins with potential diagnostic and therapeutic utility. This proteogenomics dataset represents a unique public resource for researchers and clinicians seeking to better understand and treat lung adenocarcinomas

Proteogenomic and metabolomic characterization of human glioblastoma

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment