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

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Distributed Newton Optimization with Maximized Convergence Rate

The distributed optimization problem is set up in a collection of nodes interconnected via a communication network. The goal is to find the minimizer of a global function formed by the addition of partial functions locally known at each node. A number of methods are available for addressing this problem, having different advantages. The goal of this work is to achieve the maximum possible convergence rate. As a first step towards this end, we propose a new method which we show converges faster than other available options. We then carry out a theoretical analysis which yields guarantees for convergence in a neighborhood of a local optimum and quantifies its asymptotic convergence rate. As with most distributed optimization methods, this rate depends on a step size parameter. Our second step toward our goal consists in choosing the optimal step size in the sense of maximizing the convergence rate. Since this optimal value depends on the unknown global function, we tackle the problem by proposing a fully distributed method for estimating it. We present numerical experiments showing that, for the same step size, our method converges significantly faster than its rivals. Experiments also show that the distributed step size estimation method achieves the theoretically maximum asymptotic convergence rate

Deregulation of Cholesterol Homeostasis by a Nuclear Hormone Receptor Crosstalk in Advanced Prostate Cancer

Metastatic castration-resistant prostate cancer (mCRPC) features high intratumoral cholesterol levels, due to aberrant regulation of cholesterol homeostasis. However, the underlying mechanisms are still poorly understood. The retinoid acid receptor-related orphan receptor gamma (RORγ), an attractive therapeutic target for cancer and autoimmune diseases, is strongly implicated in prostate cancer progression. We demonstrate in this study that in mCRPC cells and tumors, RORγ plays a crucial role in deregulation of cholesterol homeostasis. First, we found that RORγ activates the expression of key cholesterol biosynthesis proteins, including HMGCS1, HMGCR, and SQLE. Interestingly, we also found that RORγ inhibition induces cholesterol efflux gene program including ABCA1, ABCG1 and ApoA1. Our further studies revealed that liver X receptors (LXRα and LXRβ), the master regulators of cholesterol efflux pathway, mediate the function of RORγ in repression of cholesterol efflux. Finally, we demonstrated that RORγ antagonist in combination with statins has synergistic effect in killing mCRPC cells through blocking statin-induced feedback induction of cholesterol biosynthesis program and that the combination treatment also elicits stronger anti-tumor effects than either alone. Altogether, our work revealed that in mCRPC, RORγ contributes to aberrant cholesterol homeostasis by induction of cholesterol biosynthesis program and suppression of cholesterol efflux genes. Our findings support a therapeutic strategy of targeting RORγ alone or in combination with statin for effective treatment of mCRPC

State Estimation for Discrete Time Linear Systems with Nonlinear measurements and Round-Robin Protocol

The state estimation problem for discrete time-invariant systems with non-linear measurements over sensor networks under communication constraint is investigated in this paper. Communication in networks with constraint is that only one shared communication channel is available for message transmission at each time index, and therefore only partial measurements of sensors can be updated to the estimator. It is considered in this paper that the transmission nodes is given permissions for the communication channel on the basis of Round Robin protocol. A compensation strategy with weighted value and extended state-based form is adopted to solve the communication constraint, which transforms the system to be a linear time-variant one. Then an optimal linear filter is given based on the extended Kalman filter. Finally, simulation results are given to show the proposed filter is better than the hold on strategy by means of root mean square error.</p

Distributed Target Tracking Using Maximum Likelihood Kalman Filter with Non-Linear Measurements

We propose a distributed method for tracking a target with linear dynamics and non-linear measurements acquired by a number of sensors. The proposed method is based on a Bayesian tracking technique called maximum likelihood Kalman filter (MLKF), which is known to be asymptotically optimal, in the mean squared sense, as the number of sensors becomes large. This method requires, at each time step, the solution of a maximum likelihood (ML) estimation problem as well as the Hessian matrix of the likelihood function at the optimal. In order to obtain a distributed method, we compute the ML estimate using a recently proposed fully distributed optimization method, which yields the required Hessian matrix as a byproduct of the optimization procedure. We call the algorithm so obtained the distributed MLKF (DMLKF). Numerical simulation results show that DMLKF largely outperforms other available distributed tracking methods, in terms of tracking accuracy, and that it asymptotically approximates the optimal Bayesian tracking solution, as the number of sensors and inter-node information fusion iterations increase.Fil: Huang, Zenghong. Guangdong University of Technology; ChinaFil: Marelli, Damian Edgardo. Guangdong University of Technology; China. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Xu, Yong. Guangdong University of Technology; ChinaFil: Fu, Minyue. Universidad de Newcastle; Australi

Learning Optimal Stochastic Sensor Scheduling for Remote Estimation With Channel Capacity Constraint

Scheduling for multiple sensors to observe multiple systems is investigated. Only one sensor can transmit a measurement to the remote estimator over a Markovian fading channel at each time instant. A stochastic scheduling protocol is proposed, which first chooses the system to be observed via a probability distribution, and then chooses the sensor to transmit the measurement via another distribution. The stochastic sensor scheduling is modeled as a Markov decision process (MDP). A sufficient condition is derived to ensure the stability of remote estimation error covariance by a contraction mapping operator. In addition, the existence of an optimal deterministic and stationary policy is proved. To overcome the curse of dimensionality, the deep deterministic policy gradient, a recent deep reinforcement learning algorithm, is utilized to obtain an optimal policy for the MDP. Finally, a practical example is given to demonstrate that the developed scheduling algorithm significantly outperforms other policies.</p

Pinning synchronization for markovian jump neural networks with uncertain impulsive effects

This work concentrates on synchronization of neural networks (NNs) with Markovian parameters, where the Markov chain has partially unknown transition probabilities (PUTP). Due to the existence of interference and noise in practice, we combine the uncertain variable with the complex coupling term as the impulsive disturbance of NNs. A corresponding mode-dependent pinning controller is designed to reduce the control costs, and synchronization error system is also derived, whose impulsive update state is listed separately. A sufficient condition of synchronization for NNs is completed by constructing a Lyapunov functional candidate and a series of iterations. Because the disturbance should avoid being too frequent to guarantee synchronization of NNs, the allowed minimum interval h of the impulsive disturbance is derived. Finally, the correctness and the superiority of the developed result are illustrated by a numerical example.</p

The Effects of Sleeve Gastrectomy on Glucose Metabolism and Glucagon-Like Peptide 1 in Goto-Kakizaki Rats

Purpose. To investigate the effects of sleeve gastrectomy (SG) on glucose metabolism and changes in glucagon-like peptide 1 (GLP-1) in Goto-Kakizaki (GK) rats. Methods. GK rats were randomly assigned to one of three groups: SG, SG pair-fed plus sham surgery (PF-sham), and ad libitum-fed no surgery (control). Food intake, body weight, blood glucose, GLP-1 and insulin levels, and GLP-1 expression in the jejunum and ileum were compared. Results. The SG rats exhibited lower postoperative food intake, body weight, and fasting glucose than did the control rats (P<0.05). SG significantly improved glucose and insulin tolerance (P<0.05). Plasma GLP-1 levels were higher in SG rats than in control or PF-sham rats in the oral glucose tolerance test (OGTT) (P<0.05). Blood glucose levels expressed as a percentage of baseline were higher in SG rats than in control rats after exendin (9-39) administration (P<0.05). The levels of GLP-1 expression in the jejunum and ileum were higher in SG rats than in PF-sham and control rats (P<0.05). Conclusions. Improvement of glucose metabolism by SG was associated with increased GLP-1 secretion. SG contributes to an increase in plasma GLP-1 levels via increased GLP-1 expression in the mucosa of the jejunum and/or ileum