SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks

Anomaly detection on attributed networks aims to find the nodes whose behaviors are significantly different from other majority nodes. Generally, network data contains information about relationships between entities, and the anomaly is usually embodied in these relationships. Therefore, how to comprehensively model complex interaction patterns in networks is still a major focus. It can be observed that anomalies in networks violate the homophily assumption. However, most existing studies only considered this phenomenon obliquely rather than explicitly. Besides, the node representation of normal entities can be perturbed easily by the noise relationships introduced by anomalous nodes. To address the above issues, we present a novel contrastive learning framework for anomaly detection on attributed networks, \textbf{SCALA}, aiming to improve the embedding quality of the network and provide a new measurement of qualifying the anomaly score for each node by introducing sparsification into the conventional method. Extensive experiments are conducted on five benchmark real-world datasets and the results show that SCALA consistently outperforms all baseline methods significantly.Comment: 9 pages, 14 figure

Tetra­kis(1-ethyl-3-methyl­imidazolium) β-hexa­cosa­oxidoocta­molybdate

The title compound, (C6H11N2)4[Mo8O26] or (emim)4[β-Mo8O26] (emim is 1-ethyl-3-methyl­imidazolium), was obtained from the ionic liquid [emim]BF4. The asymmetric unit contains two [emim]+ cations and one-half of the [β-Mo8O26]4− tetra­anion, which occupies a special position on an inversion centre. The β-[Mo8O26]4− tetra­anion features eight distorted MoO6 coordination octa­hedra linked together through bridging O atoms

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.Comment: work in progress; 32 page

Non-Invasive Detection of Early-Stage Fatty Liver Disease via an On-Skin Impedance Sensor and Attention-Based Deep Learning.

Early-stage nonalcoholic fatty liver disease (NAFLD) is a silent condition, with most cases going undiagnosed, potentially progressing to liver cirrhosis and cancer. A non-invasive and cost-effective detection method for early-stage NAFLD detection is a public health priority but challenging. In this study, an adhesive, soft on-skin sensor with low electrode-skin contact impedance for early-stage NAFLD detection is fabricated. A method is developed to synthesize platinum nanoparticles and reduced graphene quantum dots onto the on-skin sensor to reduce electrode-skin contact impedance by increasing double-layer capacitance, thereby enhancing detection accuracy. Furthermore, an attention-based deep learning algorithm is introduced to differentiate impedance signals associated with early-stage NAFLD in high-fat-diet-fed low-density lipoprotein receptor knockout (Ldlr-/-) mice compared to healthy controls. The integration of an adhesive, soft on-skin sensor with low electrode-skin contact impedance and the attention-based deep learning algorithm significantly enhances the detection accuracy for early-stage NAFLD, achieving a rate above 97.5% with an area under the receiver operating characteristic curve (AUC) of 1.0. The findings present a non-invasive approach for early-stage NAFLD detection and display a strategy for improved early detection through on-skin electronics and deep learning

Comparative Genomic Analysis of Chitinase and Chitinase-Like Genes in the African Malaria Mosquito (Anopheles gambiae)

Chitinase is an important enzyme responsible for chitin metabolism in a wide range of organisms including bacteria, yeasts and other fungi, nematodes and arthropods. However, current knowledge on chitinolytic enzymes, especially their structures, functions and regulation is very limited. In this study we have identified 20 chitinase and chitinase-like genes in the African malaria mosquito, Anopheles gambiae, through genome-wide searching and transcript profiling. We assigned these genes into eight different chitinase groupings (groups I–VIII). Domain analysis of their predicted proteins showed that all contained at least one catalytic domain. However, only seven (AgCht4, AgCht5-1, AgCht6, AgCht7, AgCht8, AgCht10 and AgCht23) displayed one or more chitin-binding domains. Analyses of stage- and tissue-specific gene expression revealed that most of these genes were expressed in larval stages. However, AgCht8 was mainly expressed in the pupal and adult stages. AgCht2 and AgCht12 were specifically expressed in the foregut, whereas AgCht13 was only expressed in the midgut. The high diversity and complexity of An. gambiae chitinase and chitinase-like genes suggest their diverse functions during different developmental stages and in different tissues of the insect. A comparative genomic analysis of these genes along with those present in Drosophila melanogaster, Tribolium castaneum and several other insect species led to a uniform classification and nomenclature of these genes. Our investigation also provided important information for conducting future studies on the functions of chitinase and chitinase-like genes in this important malaria vector and other species of arthropods

Dietary intake of folate, vitamin B6, and vitamin B12, genetic polymorphism of related enzymes, and risk of breast cancer: a case-control study in Brazilian women

<p>Abstract</p> <p>Background</p> <p>Several studies have determined that dietary intake of B vitamins may be associated with breast cancer risk as a result of interactions between <it>5,10-methylenetetrahydrofolate reductase (MTHFR) </it>and <it>methionine synthase </it>(<it>MTR</it>) in the one-carbon metabolism pathway. However, the association between B vitamin intake and breast cancer risk in Brazilian women in particular has not yet been investigated.</p> <p>Methods</p> <p>A case-control study was conducted in São Paulo, Brazil, with 458 age-matched pairs of Brazilian women. Energy-adjusted intakes of folate, vitamin B₆, and vitamin B₁₂were derived from a validated Food Frequency Questionnaire (FFQ). Genotyping was completed for <it>MTHFR </it>A1298C and C677T, and <it>MTR </it>A2756G polymorphisms. A logistical regression model was used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs).</p> <p>Results</p> <p>Neither dietary intake of folate, vitamin B₆, or vitamin B₁₂nor <it>MTHFR </it>polymorphisms were independently associated with breast cancer risk. Analysis stratified by menopausal status showed a significant association between placement in the highest tertile of folate intake and risk of breast cancer in premenopausal women (OR = 2.17, 95% CI: 1.23–3.83; <it>P</it>_{<it>trend </it>}= 0.010). The <it>MTR </it>2756GG genotype was associated with a higher risk of breast cancer than the 2756AA genotype (OR = 1.99, 95% CI = 1.01–3.92; <it>P</it>_{<it>trend </it>}= 0.801), and statistically significant interactions with regard to risk were observed between the <it>MTHFR </it>A1298C polymorphism and folate (P = 0.024) or vitamin B₆(P = 0.043), and between the <it>MTHFR </it>C677T polymorphism and folate (P = 0.043) or vitamin B₁₂(P = 0.022).</p> <p>Conclusion</p> <p><it>MTHFR </it>polymorphisms and dietary intake of folate, vitamin B₆, and vitamin B₁₂had no overall association with breast cancer risk. However, increased risk was observed in total women with the <it>MTR </it>2756GG genotype and in premenopausal women with high folate intake. These findings, as well as significant interactions between <it>MTHFR </it>polymorphisms and B vitamins, warrant further investigation.</p