Towards Verifiable Text Generation with Evolving Memory and Self-Reflection

Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also known as hallucination. A promising solution to this issue is verifiable text generation, which prompts LLMs to generate content with citations for accuracy verification. However, verifiable text generation is non-trivial due to the focus-shifting phenomenon, the intricate reasoning needed to align the claim with correct citations, and the dilemma between the precision and breadth of retrieved documents. In this paper, we present VTG, an innovative framework for Verifiable Text Generation with evolving memory and self-reflection. VTG introduces evolving long short-term memory to retain both valuable documents and recent documents. A two-tier verifier equipped with an evidence finder is proposed to rethink and reflect on the relationship between the claim and citations. Furthermore, active retrieval and diverse query generation are utilized to enhance both the precision and breadth of the retrieved documents. We conduct extensive experiments on five datasets across three knowledge-intensive tasks and the results reveal that VTG significantly outperforms baselines

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error

Target Enzyme-Activated Two-Photon Fluorescent Probes:A Case Study of CYP3A4 Using a Two-Dimensional Design Strategy

The rapid development of fluorescent probes for monitoring target enzymes is still a great challenge owing to the lack of efficient ways to optimize a specific fluorophore. Herein, a practical two-dimensional strategy was designed for the development of an isoform-specific probe for CYP3A4, a key cytochrome P450 isoform responsible for the oxidation of most clinical drugs. In first dimension of the design strategy, a potential two-photon fluorescent substrate (NN) for CYP3A4 was effectively selected using ensemble-based virtual screening. In the second dimension, various substituent groups were introduced into NN to optimize the isoform-selectivity and reactivity. Finally, with ideal selectivity and sensitivity, NEN was successfully applied to the real-time detection of CYP3A4 in living cells and zebrafish. These findings suggested that our strategy is practical for developing an isoform-specific probe for a target enzyme.</p

Version-sensitive mobile app recommendation

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Molecular Design Strategy to Construct the Near-Infrared Fluorescent Probe for Selectively Sensing Human Cytochrome P450 2J2

Cytochrome P450 2J2 (CYP2J2), a key enzyme responsible for oxidative metabolism of various xenobiotics and endogenous compounds, participates in a diverse array of physiological and pathological processes in humans. Its biological role in tumorigenesis and cancer diagnosis remains poorly understood, owing to the lack of molecular tools suitable for real-time monitoring CYP2J2 in complex biological systems. Using molecular design principles we were able to modify the distance between the catalytic unit and metabolic recognition moiety, allowing us to develop a CYP2J2 selective fluorescent probe using a near-infrared fluorophore (E)-2-(2-(6-hydroxy-2, 3-dihydro-1H-xanthen-4-yl)vinyl)-3,3- dimethyl-1-propyl-3H-indol-1-ium iodide (HXPI). To improve the reactivity and isoform specificity, a self-immolative linker was introduced to the HXPI derivatives in order to better fit the narrow substrate channel of CYP2J2, the modification effectively shortened the spatial distance between the metabolic moiety (O-alkyl group) and catalytic center of CYP2J2. After screening a panel of O-alkylated HXPI derivatives, BnXPI displayed the best combination of specificity, sensitivity and applicability for detecting CYP2J2 in vitro and in vivo. Upon O-demethylation by CYP2J2, a self-immolative reaction occurred spontaneously via 1,6-elimination of p-hydroxybenzyl resulting in the release of HXPI. Allowing BnXPI to be successfully used to monitor CYP2J2 activity in real-time for various living systems including cells, tumor tissues, and tumor-bearing animals. In summary, our practical strategy could help the development of a highly specific and broadly applicable tool for monitoring CYP2J2, which offers great promise for exploring the biological functions of CYP2J2 in tumorigenesis.</p

Measurement Invariance of the Depression Anxiety Stress Scales-21 Across Gender in a Sample of Chinese University Students

The Depression Anxiety Stress Scales-21 (DASS-21) has three 7-item subscales (depression, anxiety, and stress). The current study aims assess the gender-based measurement invariance of the DASS-21 questionnaire in a Chinese university student sample from five different cities. The sample was composed of 13208 participants (62.3% female, mean age of 19.7 years, and SD age = 1.8). Multi-group confirmatory factor analysis supported full measurement invariance for the three subscales. The findings support the measurement invariance of DASS-21 scores across gender. Future research on the DASS should include additional validation across ethnicities and testing of all versions of the DASS

Molecular Design Strategy to Construct the Near-Infrared Fluorescent Probe for Selectively Sensing Human Cytochrome P450 2J2

Cytochrome P450 2J2 (CYP2J2), a key enzyme responsible for oxidative metabolism of various xenobiotics and endogenous compounds, participates in a diverse array of physiological and pathological processes in humans. Its biological role in tumorigenesis and cancer diagnosis remains poorly understood, owing to the lack of molecular tools suitable for real-time monitoring CYP2J2 in complex biological systems. Using molecular design principles, we were able to modify the distance between the catalytic unit and metabolic recognition moiety, allowing us to develop a CYP2J2 selective fluorescent probe using a near-infrared fluorophore (E)-2-(2-(6-hydroxy-2, 3-dihydro-1H-xanthen-4-yl)­vinyl)-3,3-dimethyl-1-propyl-3H-indol-1-ium iodide (HXPI). To improve the reactivity and isoform specificity, a self-immolative linker was introduced to the HXPI derivatives in order to better fit the narrow substrate channel of CYP2J2, the modification effectively shortened the spatial distance between the metabolic moiety (O-alkyl group) and catalytic center of CYP2J2. After screening a panel of O-alkylated HXPI derivatives, BnXPI displayed the best combination of specificity, sensitivity and applicability for detecting CYP2J2 in vitro and in vivo. Upon O-demethylation by CYP2J2, a self-immolative reaction occurred spontaneously via 1,6-elimination of p-hydroxybenzyl resulting in the release of HXPI. Allowing BnXPI to be successfully used to monitor CYP2J2 activity in real-time for various living systems including cells, tumor tissues, and tumor-bearing animals. In summary, our practical strategy could help the development of a highly specific and broadly applicable tool for monitoring CYP2J2, which offers great promise for exploring the biological functions of CYP2J2 in tumorigenesis