Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need

The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-based methods to learn the ID features, which tend to learn shortcuts instead of comprehensive representations. In this work, we find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We deeply explore the main contributors of OOD detection and find that reconstruction-based pretext tasks have the potential to provide a generally applicable and efficacious prior, which benefits the model in learning intrinsic data distributions of the ID dataset. Specifically, we take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD). Without bells and whistles, MOOD outperforms previous SOTA of one-class OOD detection by 5.7%, multi-class OOD detection by 3.0%, and near-distribution OOD detection by 2.1%. It even defeats the 10-shot-per-class outlier exposure OOD detection, although we do not include any OOD samples for our detectionComment: This paper is accepted by CVPR2023 and our codes are released here: https://github.com/JulietLJY/MOO

CSP and Takeout Genes Modulate the Switch between Attraction and Repulsion during Behavioral Phase Change in the Migratory Locust

Behavioral plasticity is the most striking trait in locust phase transition. However, the genetic basis for behavioral plasticity in locusts is largely unknown. To unravel the molecular mechanisms underlying the behavioral phase change in the migratory locust Locusta migratoria, the gene expression patterns over the time courses of solitarization and gregarization were compared by oligonucleotide microarray analysis. Data analysis revealed that several gene categories relevant to peripheral olfactory perception are strongly regulated in a total of 1,444 differentially expressed genes during both time courses. Among these candidate genes, several CSP (chemosensory protein) genes and one takeout gene, LmigTO1, showed higher expression in gregarious and solitarious locusts, respectively, and displayed opposite expression trends during solitarization and gregarization. qRT-PCR experiments revealed that most CSP members and LmigTO1 exhibited antenna-rich expressions. RNA interference combined with olfactory behavioral experiments confirmed that the CSP gene family and one takeout gene, LmigTO1, are involved in the shift from repulsion to attraction between individuals during gregarization and in the reverse transition during solitarization. These findings suggest that the response to locust-emitted olfactory cues regulated by CSP and takeout genes is involved in the behavioral phase change in the migratory locust and provide a previously undescribed molecular mechanism linked to the formation of locust aggregations

Functional Haplotypes of the hTERT Gene, Leukocyte Telomere Length Shortening, and the Risk of Peripheral Arterial Disease

Background: The development of peripheral arterial disease (PAD) is heterogeneous even in the presence of similar risk factors. Our aim was to determine whether inter-individual differences in leukocyte telomere length contribute to the susceptibility of PAD. Methods A total of 485 patients with PAD (defined by the ankle-brachial index) and 970 age- and gender-matched controls were recruited from seven rural communities in Henan Province in China. The relative leukocyte telomere length was determined by a quantitative PCR-based method. Two common promoter variants of the hTERT gene were genotyped to assess their effects on telomere length and the risk of PAD. In vivo luciferase assay was performed to study the transcriptional activity. Results: After adjustment for vascular risk factors and genetic variants in the hTERT gene, individuals in the lowest and middle tertiles of telomere length had a significantly higher risk of PAD than did those in the highest tertile (odds ratio [OR] 1.73, 95% confidence interval [CI] 1.29–2.49 in the middle tertile; 3.15, 95%CI 2.31–4.29 in the lowest tertile). Haplotype analysis using the 2 variants (rs2735940 and rs2853669) showed that subjects with the at-risk C-C haplotype had shorter telomere length than those individuals with the T-T haplotype and consistently had 1.30-fold (OR 1.30, 95%CI 1.06–1.58; P = 0.005) increased risk for PAD. The C-C haplotype had 43% lowered transcription activity of hTERT promoter (P<0.001). Conclusion: The associations between the functional haplotype of hTERT gene and telomere length and the risk of atherosclerotic PAD suggested that mean leukocyte telomere length may independently serve as a potential predictor of PAD

COVID-19 disease identification network based on weakly supervised feature selection

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance

Recent Update on the Pharmacological Effects and Mechanisms of Dihydromyricetin

As the most abundant natural flavonoid in rattan tea, dihydromyricetin (DMY) has shown a wide range of pharmacological effects. In addition to the general characteristics of flavonoids, DMY has the effects of cardioprotection, anti-diabetes, hepatoprotection, neuroprotection, anti-tumor, and dermatoprotection. DMY was also applied for the treatment of bacterial infection, osteoporosis, asthma, kidney injury, nephrotoxicity and so on. These effects to some extent enrich the understanding about the role of DMY in disease prevention and therapy. However, to date, we still have no outlined knowledge about the detailed mechanism of DMY, which might be related to anti-oxidation and anti-inflammation. And the detailed mechanisms may be associated with several different molecules involved in cellular apoptosis, oxidative stress, and inflammation, such as AMP-activated protein kinase (AMPK), mitogen-activated protein kinase (MAPK), protein kinase B (Akt), nuclear factor-κB (NF-κB), nuclear factor E2-related factor 2 (Nrf2), ATP-binding cassette transporter A1 (ABCA1), peroxisome proliferator-activated receptor-γ (PPARγ) and so on. Here, we summarized the current pharmacological developments of DMY as well as possible mechanisms, aiming to push the understanding about the protective role of DMY as well as its preclinical assessment of novel application

m6A regulators featured by tumor immune microenvironment landscapes and correlated with immunotherapy in non-small cell lung cancer (NSCLC)

IntroductionRecent research has confirmed the critical role that epigenetic factors play in regulating the immune response. Nonetheless, what role m6A methylation modification might play in the immune response of non-small cell lung cancer (NSCLC) remains vague.MethodsHerein, the gene expression, copy number variations (CNVs), and somatic mutations of 31 m6A regulators in NSCLC and adjacent control samples from the GEO and TCGA databases were comprehensively explored. Using consensus clustering, m6A modification patterns were identified. Correlations between m6A modification patterns and immune cell infiltration traits in the tumor immune microenvironment (TME) were systematically analyzed. Differentially expressed genes were verified and screened by random forest and cox regression analysis by comparing different m6A modification patterns. Based on the retained gene panel, a risk model was built, and m6Ascore for each sample was calculated. The function of m6Ascore in NSCLC prognosis, tumor somatic mutations, and chemotherapy/immunotherapy response prediction were evaluated.ResultsConsensus clustering classified all NSCLC samples into two m6A clusters (m6A_clusterA and m6A_clusterB) according to the expression levels of 25 m6A regulator genes. Hierarchical clustering further divides the NSCLC samples into two m6A gene clusters: m6AgeneclusterA and m6AgeneclusterB. A panel of 83 genes was screened from the 194 differentially expressed genes between m6A gene clusters. Based on this, a risk score model was established. m6A modification clusters, m6A gene clusters, and m6Ascore calculated from the risk model were able to predict tumor stages, immune cell infiltration, clinical prognosis, and tumor somatic mutations. NSCLC patients with high m6Ascore have poor drug resistance to chemotherapy drugs (Cisplatin and Gemcitabine) and exhibit considerable therapeutic benefits and favorable clinical responses to anti-PD1 or anti-CTLA4 immunotherapy.DiscussionIn conclusion, methylation modification patterns mediated by the m6A regulators in individuals play a non-negligible role in prognosis prediction and immunotherapy response, which will facilitate personalized treatment and immunotherapeutic strategies for NSCLC patients in the future