PFKFB3 Control of Cancer Growth by Responding to Circadian Clock Outputs

Circadian clock dysregulation promotes cancer growth. Here we show that PFKFB3, the gene that encodes for inducible 6-phosphofructo-2-kinase as an essential supporting enzyme of cancer cell survival through stimulating glycolysis, mediates circadian control of carcinogenesis. In patients with tongue cancers, PFKFB3 expression in both cancers and its surrounding tissues was increased significantly compared with that in the control, and was accompanied with dys-regulated expression of core circadian genes. In the in vitro systems, SCC9 tongue cancer cells displayed rhythmic expression of PFKFB3 and CLOCK that was distinct from control KC cells. Furthermore, PFKFB3 expression in SCC9 cells was stimulated by CLOCK through binding and enhancing the transcription activity of PFKFB3 promoter. Inhibition of PFKFB3 at zeitgeber time 7 (ZT7), but not at ZT19 caused significant decreases in lactate production and in cell proliferation. Consistently, PFKFB3 inhibition in mice at circadian time (CT) 7, but not CT19 significantly reduced the growth of implanted neoplasms. Taken together, these findings demonstrate PFKFB3 as a mediator of circadian control of cancer growth, thereby highlighting the importance of time-based PFKFB3 inhibition in cancer treatment.China National Science Foundation [31110103905, 31422022]; National Institutes of Health [HL108922, HL095556, R01DK095828, R01DK095862]; Hatch Program of the National Institutes of Food and Agriculture (NIFA)SCI(E)[email protected]; [email protected]

Discovery of TIGIT inhibitors based on DEL and machine learning

Drug discovery has entered a new period of vigorous development with advanced technologies such as DNA-encoded library (DEL) and artificial intelligence (AI). The previous DEL-AI combination has been successfully applied in the drug discovery of classical kinase and receptor targets mainly based on the known scaffold. So far, there is no report of the DEL-AI combination on inhibitors targeting protein-protein interaction, including those undruggable targets with few or unknown active scaffolds. Here, we applied DEL technology on the T cell immunoglobulin and ITIM domain (TIGIT) target, resulting in the unique hit compound 1 (IC50 = 20.7 μM). Based on the screening data from DEL and hit derivatives a1-a34, a machine learning (ML) modeling process was established to address the challenge of poor sample distribution uniformity, which is also frequently encountered in DEL screening on new targets. In the end, the established ML model achieved a satisfactory hit rate of about 75% for derivatives in a high-scored area

Palmitoleate Induces Hepatic Steatosis but Suppresses Liver Inflammatory Response in Mice

The interaction between fat deposition and inflammation during obesity contributes to the development of non-alcoholic fatty liver disease (NAFLD). The present study examined the effects of palmitoleate, a monounsaturated fatty acid (16∶1n7), on liver metabolic and inflammatory responses, and investigated the mechanisms by which palmitoleate increases hepatocyte fatty acid synthase (FAS) expression. Male wild-type C57BL/6J mice were supplemented with palmitoleate and subjected to the assays to analyze hepatic steatosis and liver inflammatory response. Additionally, mouse primary hepatocytes were treated with palmitoleate and used to analyze fat deposition, the inflammatory response, and sterol regulatory element-binding protein 1c (SREBP1c) activation. Compared with controls, palmitoleate supplementation increased the circulating levels of palmitoleate and improved systemic insulin sensitivity. Locally, hepatic fat deposition and SREBP1c and FAS expression were significantly increased in palmitoleate-supplemented mice. These pro-lipogenic events were accompanied by improvement of liver insulin signaling. In addition, palmitoleate supplementation reduced the numbers of macrophages/Kupffer cells in livers of the treated mice. Consistently, supplementation of palmitoleate decreased the phosphorylation of nuclear factor kappa B (NF-κB, p65) and the expression of proinflammatory cytokines. These results were recapitulated in primary mouse hepatocytes. In terms of regulating FAS expression, treatment of palmitoleate increased the transcription activity of SREBP1c and enhanced the binding of SREBP1c to FAS promoter. Palmitoleate also decreased the phosphorylation of NF-κB p65 and the expression of proinflammatory cytokines in cultured macrophages. Together, these results suggest that palmitoleate acts through dissociating liver inflammatory response from hepatic steatosis to play a unique role in NAFLD

Effects of short-distance transportation on physiological indexes, intestinal morphology, microbial community, and the transcriptome of the jejunum in weaned piglets

Transportation of livestock is unavoidable in animal production. A total of 72 piglets were randomly divided into the CON group and the TSG group, and the piglets in CON group were transported for two hours. The purpose of this study was to determine the effects of short-distance road transportation lasting 2 h on the jejunum of weaned piglets. Our results showed that compared with the control group, there was no impact on the growth performance of piglets in the transport group (P > 0.05). The concentrations of cortisol, heat shock protein (HSP)70, HSP90, C-reactive protein, interleukin (IL)-6, IL-8, IL-12, and interferon-γ and the activity of reactive oxygen species were increased in the jejunum of piglets in the transport group (P < 0.05 compared with the control group). The concentrations of glutathione peroxidase, claudin-1, occludin, and zonula occludens-1 showed no between-group differences (P > 0.05). Regarding intestinal morphology, the transport group showed infiltration of a small amount of lymphocytes into the jejunum mucosa epithelium that was accompanied by edema of the lamina propria, whereas the control group showed no obvious abnormalities. At the genus level, in the transport group, the 16S rRNA sequencing revealed a downward trend in the relative abundance of Lactobacillus and an upward trend in the relative abundance of Muribaculaceae_unclassified. There was also increased mRNA expression of genes associated with inflammation in the transport group, but the genes and pathways related to apoptosis were not activated. In summary, weaned piglets undergoing 2 h of short-distance road transportation showed stress and inflammatory reactions of the jejunum but did not exhibit oxidative damage or activation of the apoptosis pathway of the jejunum. Furthermore, the growth performance of the piglets was not affected by the trip

A Fast Circle Detector with Efficient Arc Extraction

Circle detection is a crucial problem in computer vision and pattern recognition. Improving the accuracy and efficiency of circle detectors has important scientific significance and excellent application value. In this paper, we propose a circle detection method with efficient arc extraction. In order to reduce edge redundancy and eliminate crossing points, we present an edge refinement algorithm to refine the edges into single-pixel-wide branchless contour curves. To address the contour curve segmentation difficulty, we improved the CTAR (Chord to Triangular Arms Ratio) corner detection method to enhance corner point detection and segment the contour curves based on corner points. Then, we used the relative position constraint of arcs to improve the circle detection accuracy further. Finally, we verified the feasibility and reliability of the proposed method by comparing our approach with five other methods using three datasets. The experimental results showed that the presented method had the advantages of anti-obscuration, anti-defect, and real-time performance over other methods

Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

A symmetry-based hybrid precoder and combiner is a high spectral efficiency structure in millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system. To improve the spectral efficiency of the mmWave mMIMO-NOMA system, we first propose a user grouping scheme to suppress the strong inter-user interference caused by NOMA, then the hybrid precoder based on user channel alignment and the zero-forcing algorithm is constructed to further improve the signal-to-interference-plus-noise ratio (SINR) of the receiver. Subsequently, the non-convex spectral efficiency optimization problem is transformed into a convex optimization problem of inter-cluster power allocation and the closed-form solution for the optimal power under the minimum rate constraint is obtained by solving the KKT condition to further improve the spectral efficiency. The simulation results show that the proposed scheme can achieve higher spectral efficiency compared to orthogonal multiple access (OMA), fixed power allocation (FPA), K-means, and cluster head selection (CHS)

Hybrid Precoding-Based Millimeter Wave Massive MIMO-NOMA Systems

A symmetry-based hybrid precoder and combiner is a high spectral efficiency structure in millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system. To improve the spectral efficiency of the mmWave mMIMO-NOMA system, we first propose a user grouping scheme to suppress the strong inter-user interference caused by NOMA, then the hybrid precoder based on user channel alignment and the zero-forcing algorithm is constructed to further improve the signal-to-interference-plus-noise ratio (SINR) of the receiver. Subsequently, the non-convex spectral efficiency optimization problem is transformed into a convex optimization problem of inter-cluster power allocation and the closed-form solution for the optimal power under the minimum rate constraint is obtained by solving the KKT condition to further improve the spectral efficiency. The simulation results show that the proposed scheme can achieve higher spectral efficiency compared to orthogonal multiple access (OMA), fixed power allocation (FPA), K-means, and cluster head selection (CHS)

A Semi-Supervised Semantic Segmentation Method for Blast-Hole Detection

The goal of blast-hole detection is to help place charge explosives into blast-holes. This process is full of challenges, because it requires the ability to extract sample features in complex environments, and to detect a wide variety of blast-holes. Detection techniques based on deep learning with RGB-D semantic segmentation have emerged in recent years of research and achieved good results. However, implementing semantic segmentation based on deep learning usually requires a large amount of labeled data, which creates a large burden on the production of the dataset. To address the dilemma that there is very little training data available for explosive charging equipment to detect blast-holes, this paper extends the core idea of semi-supervised learning to RGB-D semantic segmentation, and devises an ERF-AC-PSPNet model based on a symmetric encoder–decoder structure. The model adds a residual connection layer and a dilated convolution layer for down-sampling, followed by an attention complementary module to acquire the feature maps, and uses a pyramid scene parsing network to achieve hole segmentation during decoding. A new semi-supervised learning method, based on pseudo-labeling and self-training, is proposed, to train the model for intelligent detection of blast-holes. The designed pseudo-labeling is based on the HOG algorithm and depth data, and proved to have good results in experiments. To verify the validity of the method, we carried out experiments on the images of blast-holes collected at a mine site. Compared to the previous segmentation methods, our method is less dependent on the labeled data and achieved IoU of 0.810, 0.867, 0.923, and 0.945, at labeling ratios of 1/8, 1/4, 1/2, and 1