Spherical two-distance sets and eigenvalues of signed graphs

We study the problem of determining the maximum size of a spherical two-distance set with two fixed angles (one acute and one obtuse) in high dimensions. Let $N_{\alpha,\beta}(d)$ denote the maximum number of unit vectors in $\mathbb R^d$ where all pairwise inner products lie in $\{\alpha,\beta\}$. For fixed $-1\leq\beta<0\leq\alpha<1$, we propose a conjecture for the limit of $N_{\alpha,\beta}(d)/d$ as $d \to \infty$ in terms of eigenvalue multiplicities of signed graphs. We determine this limit when $\alpha+2\beta<0$ or $(1-\alpha)/(\alpha-\beta) \in \{1, \sqrt{2}, \sqrt{3}\}$. Our work builds on our recent resolution of the problem in the case of $\alpha = -\beta$ (corresponding to equiangular lines). It is the first determination of $\lim_{d \to \infty} N_{\alpha,\beta}(d)/d$ for any nontrivial fixed values of $\alpha$ and $\beta$ outside of the equiangular lines setting.Comment: 21 pages, 8 figure

Two-Year Change in Blood Pressure Status and Left Ventricular Mass Index in Chinese Children

Background: Elevated blood pressure (BP) is associated with target organ damage, such as left ventricular hypertrophy (LVH), in childhood. However, it is unclear if children who resolve elevated BP have reduced levels of left ventricular mass index (LVMI). This study aimed to examine the association between change in BP status over 2 years and LVMI among Chinese children. Methods: Data were from 1,183 children aged 6-11 years at baseline in 2017 who were followed up in 2019 in the Huantai Childhood Cardiovascular Health Cohort Study. Change in BP status over 2 years from baseline to follow-up was categorized as: persistent normal BP, resolved elevated BP (elevated BP at baseline, normal BP at follow-up), incident elevated BP (normal BP at baseline, elevated BP at follow-up), and persistent elevated BP. Elevated BP status was defined according to national reference standards as systolic or diastolic BP levels >= sex-, age-, and height-specific 95th percentiles. Results: LVMI levels were lowest in children with persistent normal BP (30.13 g/m(2.7)), higher in those with incident elevated BP (31.27 g/m(2.7)), and highest in those with persistent elevated BP (33.26 g/m(2.7)). However, LVMI levels in those who had resolved elevated BP (30.67 g/m(2.7)) were similar to those with persistent normal BP. In the fully adjusted model, compared with children with persistent normal BP, those with persistent elevated BP and incident elevated BP had higher LVMI at follow-up (ss = 3.131, p Conclusion: Developing or maintaining elevated BP over a 2-year period in childhood associated with higher levels of LVMI, but those able to resolve their elevated BP status over the same period had LVMI levels that were similar with those who had normal BP at both time points. Thus, it is important to identify children with elevated BP at early time and to take effective measures to lower their BP levels, thereby reducing high LVMI levels and related cardiovascular diseases in the future.</p

Expression of EPO and related factors in the liver and kidney of plain and Tibetan sheep

Erythropoietin (EPO), hypoxia-inducible factor-1α (HIF-1α), hypoxia-inducible factor-2α (HIF2α), and vascular endothelial growth factor (VEGF) are key factors in the regulation of hypoxia, and can transcriptionally activate multiple genes under hypoxic conditions, thereby initiating large hypoxic stress in the network. The liver and kidneys are important metabolic organs of the body. We assessed the expression of EPO, HIF-1α, HIF-2α, and VEGF in liver and kidney tissues of plain and Tibetan sheep using hematoxylin and eosin staining, immunohistochemistry, and RT-qPCR. The results showed that EPO, HIF-1α, HIF-2α, and VEGF were expressed in tubular epithelial cells, collecting duct epithelial cells, mural epithelial cells, and the glomerular cytoplasm of Tibetan sheep, and their expression was significantly higher in Tibetan sheep than in plain sheep (P<0.05). EPO, HIF-1α, HIF-2α, and VEGF are expressed in hepatocytes, interlobular venous endothelial cells, and interlobular bile duct epithelial cells. In plain sheep, positive signals for EPO, HIF-1α, HIF-2α, and VEGF were localized mainly in interlobular venous endothelial cells, whereas VEGF and HIF-2α were negatively expressed in interlobular bile duct epithelial cells and positively expressed in EPO and HIF-1α. The differences in EPO, HIF-1α, and HIF-2α in Tibetan sheep were significantly higher than those in plain sheep (P<0.001). In the liver and kidney tissues of Tibetan sheep, EPO was associated with HIF-1α, HIF-2α, and VEGF (P<0.05). RT-qPCR results showed that EPO was not expressed, and HIF-1α, HIF-2α, and VEGF were expressed (P<0.05). The results showed that the expression of EPO, HIF-1α, HIF-2α, and VEGF in the kidney and liver of Tibetan sheep was higher than that in of plain sheep. Therefore, EPO, HIF-1α, HIF-2α, and VEGF may be involved in the adaptive response of plateau animals, which provides theoretical clarity to further explore the adaptive mechanism of plateau hypoxia in Tibetan sheep

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS

M2 macrophage inhibits the antitumor effects of Lenvatinib on intrahepatic cholangiocarcinoma

Background and objectivesThe relationship between the tumor microenvironment and the network of key signaling pathways in cancer plays a key role in the occurrence and development of tumors. Tumor-associated macrophages (TAMs) are important inflammatory cells in the tumor microenvironment and play an important role in tumorigenesis and progression. Macrophages in malignant tumors, mainly the M2 subtype, promote tumor progression by producing cytokines and down-regulating anti-inflammatory immune responses. Several articles have investigated the effect of macrophages on the sensitivity of cancer chemotherapeutic agents, but few such articles have been reported in cholangiocarcinoma, so we investigated the effect of M2 macrophage on the sensitivity of cholangiocarcinoma cells to Lenvatinib compared to M1.MethodsTHP-1 monocytes were polarized to M0 macrophage by phorbol 12-myristate 13-acetate (PMA) and then induced to differentiate into M1 and M2 macrophages by LPS, IFN-γ and IL-4 and IL-13, respectively. Macrophages and cholangiocarcinoma cells were co-cultured prior to 24 hours of Lenvatinib administration, cancer cell apoptosis was detected by western-blot, FACS analysis of Annexin V and PI staining. Furthermore, we use xCELLigence RTCA SP Instrument (ACEA Bio-sciences) to monitor cell viability of Lenvatinib administration in co-culture of cholangiocarcinoma cells and macrophages. After tumorigenesis in immunodeficient mice, Lenvatinib was administered, and the effects of M2 on biological characteristics of cholangiocarcinoma cells were investigated by immuno-histochemistry.ResultsmRNA and protein expression of M1 and M2 markers confirmed the polarization of THP-1 derived macrophages, which provided a successful and efficient model of monocyte polarization to TAMs. Lenvatinib-induced apoptosis of cholangiocarcinoma cells was significantly reduced when co-cultured with M2 macrophage, whereas apoptosis of cholangiocarcinoma cells co-cultured with M1 macrophage was increased. In the CDX model, Lenvatinib-induced cancer cell apoptosis was markedly reduced, and proliferative cells increased in the presence of M2 macrophages. Angiogenesis related factors was significantly increased in cholangiocarcinoma cells co-cultured with M2.ConclusionCompared with M1, M2 macrophages can inhibit the anti-tumor effect of Lenvatinib on cholangiocarcinoma through immune regulation, which may be related to the tumor angiogenesis factor effect of M2 macrophage

Whole-Genome Sequencing analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease

A Framework For Detecting Noncoding Rare-Variant associations of Large-Scale Whole-Genome Sequencing Studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 toPMed samples. We also analyze five non-lipid toPMed traits

A New Framework for Visual Classification of Multi-Channel Malware Based on Transfer Learning

With the continuous development and popularization of the Internet, there has been an increasing number of network security problems appearing. Among them, the rapid growth in the number of malware and the emergence of variants have seriously affected the security of the Internet. Traditional malware detection methods require heavy feature engineering, which seriously affects the efficiency of detection. Existing deep-learning-based malware detection methods have problems such as poor generalization ability and long training time. Therefore, we propose a malware classification method based on transfer learning for multi-channel image vision features and ResNet convolutional neural networks. Firstly, the features of malware samples are extracted and converted into grayscale images of three different types. Then, the grayscale image sizes are processed using the bilinear interpolation algorithm to make them uniform in size. Finally, the three grayscale images are synthesized into three-dimensional RGB images, and the RGB images processed using data enhancement are used for training and classification. For the classification model, we used the previous ImageNet dataset (>10 million) and trained all the parameters of ResNet after loading the weights. For the evaluations, an experiment was conducted using the Microsoft BIG benchmark dataset. The experimental results showed that the accuracy on the Microsoft dataset reached 99.99%. We found that our proposed method can better extract the texture features of malware, effectively improve the accuracy and detection efficiency, and outperform the compared models on all performance metrics