RORγ is a targetable master regulator of cholesterol biosynthesis in a cancer subtype.

Tumor subtype-specific metabolic reprogrammers could serve as targets of therapeutic intervention. Here we show that triple-negative breast cancer (TNBC) exhibits a hyper-activated cholesterol-biosynthesis program that is strongly linked to nuclear receptor RORγ, compared to estrogen receptor-positive breast cancer. Genetic and pharmacological inhibition of RORγ reduces tumor cholesterol content and synthesis rate while preserving host cholesterol homeostasis. We demonstrate that RORγ functions as an essential activator of the entire cholesterol-biosynthesis program, dominating SREBP2 via its binding to cholesterol-biosynthesis genes and its facilitation of the recruitment of SREBP2. RORγ inhibition disrupts its association with SREBP2 and reduces chromatin acetylation at cholesterol-biosynthesis gene loci. RORγ antagonists cause tumor regression in patient-derived xenografts and immune-intact models. Their combination with cholesterol-lowering statins elicits superior anti-tumor synergy selectively in TNBC. Together, our study uncovers a master regulator of the cholesterol-biosynthesis program and an attractive target for TNBC

Strand antagonism in RNAi: an explanation of differences in potency between intracellularly expressed siRNA and shRNA

Strategies to regulate gene function frequently use small interfering RNAs (siRNAs) that can be made from their shRNA precursors via Dicer. However, when the duplex components of these siRNA effectors are expressed from their respective coding genes, the RNA interference (RNAi) activity is much reduced. Here, we explored the mechanisms of action of shRNA and siRNA and found the expressed siRNA, in contrast to short hairpin RNA (shRNA), exhibits strong strand antagonism, with the sense RNA negatively and unexpectedly regulating RNAi. Therefore, we altered the relative levels of strands of siRNA duplexes during their expression, increasing the level of the antisense component, reducing the level of the sense component, or both and, in this way we were able to enhance the potency of the siRNA. Such vector-delivered siRNA attacked its target effectively. These findings provide new insight into RNAi and, in particular, they demonstrate that strand antagonism is responsible for making siRNA far less potent than shRNA

Pyrvinium Targets the Unfolded Protein Response to Hypoglycemia and Its Anti-Tumor Activity Is Enhanced by Combination Therapy

We identified pyrvinium pamoate, an old anthelminthic medicine, which preferentially inhibits anchorage-independent growth of cancer cells over anchorage-dependent growth (∼10 fold). It was also reported by others to have anti-tumor activity in vivo and selective toxicity against cancer cells under glucose starvation in vitro, but with unknown mechanism. Here, we provide evidence that pyrvinium suppresses the transcriptional activation of GRP78 and GRP94 induced by glucose deprivation or 2-deoxyglucose (2DG, a glycolysis inhibitor), but not by tunicamycin or A23187. Other UPR pathways induced by glucose starvation, e.g. XBP-1, ATF4, were also found suppressed by pyrvinium. Constitutive expression of GRP78 via transgene partially protected cells from pyrvinium induced cell death under glucose starvation, suggesting that suppression of the UPR is involved in pyrvinium mediated cytotoxicity under glucose starvation. Xenograft experiments showed rather marginal overall anti-tumor activity for pyrvinium as a monotherapy. However, the combination of pyrvinium and Doxorubicin demonstrated significantly enhanced efficacy in vivo, supporting a mechanistic treatment concept based on tumor hypoglycemia and UPR

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Cause of Death and Predictors of All-Cause Mortality in Anticoagulated Patients With Nonvalvular Atrial Fibrillation : Data From ROCKET AF

M. Kaste on työryhmän ROCKET AF Steering Comm jäsen.Background-Atrial fibrillation is associated with higher mortality. Identification of causes of death and contemporary risk factors for all-cause mortality may guide interventions. Methods and Results-In the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) study, patients with nonvalvular atrial fibrillation were randomized to rivaroxaban or dose-adjusted warfarin. Cox proportional hazards regression with backward elimination identified factors at randomization that were independently associated with all-cause mortality in the 14 171 participants in the intention-to-treat population. The median age was 73 years, and the mean CHADS(2) score was 3.5. Over 1.9 years of median follow-up, 1214 (8.6%) patients died. Kaplan-Meier mortality rates were 4.2% at 1 year and 8.9% at 2 years. The majority of classified deaths (1081) were cardiovascular (72%), whereas only 6% were nonhemorrhagic stroke or systemic embolism. No significant difference in all-cause mortality was observed between the rivaroxaban and warfarin arms (P=0.15). Heart failure (hazard ratio 1.51, 95% CI 1.33-1.70, P= 75 years (hazard ratio 1.69, 95% CI 1.51-1.90, P Conclusions-In a large population of patients anticoagulated for nonvalvular atrial fibrillation, approximate to 7 in 10 deaths were cardiovascular, whereasPeer reviewe

Experience Sharing Based Memetic Transfer Learning for Multiagent Reinforcement Learning

Wang T, Peng X, Jin Y, Xu D. Experience Sharing Based Memetic Transfer Learning for Multiagent Reinforcement Learning. Memetic Computing. 2022;14(1):3-17.In transfer learning (TL) for multiagent reinforcement learning (MARL), most popular methods are based on action advising scheme, in which skilled agents directly transfer actions, i.e., explicit knowledge, to other agents. However, this scheme requires an inquiry-answer process, which quadratically increases the computational load as the number of agents increases. To enhance the scalability of TL for MARL when all the agents learn from scratch, we propose an experience sharing based memetic TL for MARL, called MeTL-ES. In the MeTL-ES, the agents actively share implicit memetic knowledge (experience), which avoids the inquiry-answer process and brings highly scalable and effective acceleration of learning. In particular, we firstly design an experience sharing scheme to share implicit meme based experience among the agents. Within this scheme, experience from the peers is collected and used to speed up the learning process. More importantly, this scheme frees the agents from actively asking for the states and policies of other agents, which enhances scalability. Secondly, an event-triggered scheme is designed to enable the agents to share the experiences at appropriate timings. Simulation studies show that, compared with the existing methods, the proposed MeTL-ES can more effectively enhance the learning speed of learning-from-scratch MARL systems. At the same time, we show that the communication cost and computational load of MeTL-ES increase linearly with the growth of the number of agents, indicating better scalability compared to the popular action advising based methods

Cooperative Coevolutionary CMA-ES With Landscape-Aware Grouping in Noisy Environments

Wu Y, Peng X, Wang H, Jin Y, Xu D. Cooperative Coevolutionary CMA-ES With Landscape-Aware Grouping in Noisy Environments. IEEE Transactions on Evolutionary Computation. 2023;27(3):686-700.Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision variables. In reality, many problems are high dimensional. Cooperative coevolutionary (CC) algorithms based on a divide-and-conquer strategy are promising in solving complex high-dimensional problems. However, noisy fitness evaluations pose a challenge in problem decomposition for CC. The state-of-the-art grouping methods, such as differential grouping (DG) and recursive DG, are unable to work properly in noisy environments. Because it is impossible to distinguish whether the change of one variable’s difference value is caused by noise or the perturbation of its interacting variables. As a result, every pair of variables will be identified as nonseparable in these methods. In this article, we study how to group decision variables with the covariance matrix adaptation evolution strategy (CMA-ES) in noisy environments and subsequently propose a landscape-aware grouping (LAG) method. Instead of detecting pairwise interacting variables, we directly identify a nonseparable subcomponent. To this end, we propose to use two convergence features: 1) variable convergence time and 2) accumulative path, to describe variables’ fitness landscapes; then, variables are clustered according to these two features. Numerical experiments show that LAG can more effectively identify interactive decision variables in the presence of multiplicative noise than the DG and some of its variants. Up to 500 dimensions, the performance of CC CMA-ES with landscape-aware grouping (CC-CMAES-LAG) is competitive compared with existing CC algorithms and uncertainty-handling CMA-ES (UH-CMA-ES)

Effect of C1q/TNF-Related Protein 9 on Coronary Artery Calcification: An Observational Study

Coronary artery calcification (CAC) increases the risk of acute coronary syndrome. This study examined the correlation between C1q/TNF-related protein 9 (CTRP9) and CAC and explored CTRP9 as a biomarker for prognosis. We divided 275 patients with coronary heart disease into four groups. In order to balance the baseline confounding factors, propensity score matching (PSM) was performed to match CAC patients with non-CAC patients in a 1:1 ratio. Optical coherence tomography (OCT) calcification scoring was performed in 126 patients with CAC. Moreover, 140 patients who underwent OCT were followed-up for 9 months for analysis of the correlation between CTRP9 levels and clinical prognosis. Based on OCT calcification scores, 126 patients with CAC were divided into the 0–2 and 3–4 groups. Plasma CTRP9 levels were significantly lower in the type 2 diabetes mellitus (T2DM), CAC and CAC with T2DM groups than in the control group. CTRP9 played roles as a protective factor and potential predictor in CAC severity. The AUC of the OCT calcification score 3–4 group predicted by the plasma CTRP9 level was 0.766. During the follow-up period, the cumulative event-free survival rate was significantly lower in the low-level CTRP9 (L-CTRP9) group than in the high-level (H-CTRP9) group, and the incidence of major endpoint events was significantly higher in the L-CTRP9 group than in the H-CTRP9 group. CTRP9 can be a valuable biomarker for CAC occurrence and severity and can predict patients’ clinical prognosis