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Gunslinging justice examines gun violence in Western films and literature alongside changes in justifiable homicide and gun rights in the United States.

The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).

A well-established role of cyclic GMP-AMP synthase (cGAS) is the recognition of cytosolic DNA, which is linked to the activation of host defense programs against pathogens via stimulator of interferon genes (STING)-dependent innate immune response. Recent advance has also revealed that cGAS may be involved in several noninfectious contexts by localizing to subcellular compartments other than the cytosol. However, the subcellular localization and function of cGAS in different biological conditions is unclear; in particular, its role in cancer progression remains poorly understood. Here we show that cGAS is localized to mitochondria and protects hepatocellular carcinoma cells from ferroptosis in vitro and in vivo. cGAS anchors to the outer mitochondrial membrane where it associates with dynamin-related protein 1 (DRP1) to facilitate its oligomerization. In the absence of cGAS or DRP1 oligomerization, mitochondrial ROS accumulation and ferroptosis increase, inhibiting tumor growth. Collectively, this previously unrecognized role for cGAS in orchestrating mitochondrial function and cancer progression suggests that cGAS interactions in mitochondria can serve as potential targets for new cancer interventions.

The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-in-robotics aspects: simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This Perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic at hand. The meeting brought together participants from a range of organizations, disciplines, and application fields, with expertise at the intersection of robotics, machine learning, and physics-based simulation.

The aim of this research is to determine the association between physical activity and basic fitness tests in female junior ROTU. This research is a descriptive quantitative research using IPAQ and it involves a 2.4 km run test, push-up and sit-up. A total of 103 respondents from ROTU, NDUM 2017 session were selected at random for this research. The findings showed that inactive physical activity led to failed basic fitness tests. There is an intention of investigating the association between physical activity and basic fitness tests in female senior and intermediate ROTU.

Glucocorticoid treatment is recommended as a standard of care in Duchenne muscular dystrophy; however, few studies have assessed the long-term benefits of this treatment. We examined the long-term effects of glucocorticoids on milestone-related disease progression across the lifespan and survival in patients with Duchenne muscular dystrophy. For this prospective cohort study, we enrolled male patients aged 2–28 years with Duchenne muscular dystrophy at 20 centres in nine countries. Patients were followed up for 10 years. We compared no glucocorticoid treatment or cumulative treatment duration of less than 1 month versus treatment of 1 year or longer with regard to progression of nine disease-related and clinically meaningful mobility and upper limb milestones. We used Kaplan-Meier analyses to compare glucocorticoid treatment groups for time to stand from supine of 5 s or longer and 10 s or longer, and loss of stand from supine, four-stair climb, ambulation, full overhead reach, hand-to-mouth function, and hand function. Risk of death was also assessed. This study is registered with ClinicalTrials.gov, number NCT00468832. 440 patients were enrolled during two recruitment periods (2006–09 and 2012–16). Time to all disease progression milestone events was significantly longer in patients treated with glucocorticoids for 1 year or longer than in patients treated for less than 1 month or never treated (log-rank p<0·0001). Glucocorticoid treatment for 1 year or longer was associated with increased median age at loss of mobility milestones by 2·1–4·4 years and upper limb milestones by 2·8–8·0 years compared with treatment for less than 1 month. Deflazacort was associated with increased median age at loss of three milestones by 2·1–2·7 years in comparison with prednisone or prednisolone (log-rank p<0·012). 45 patients died during the 10-year follow-up. 39 (87%) of these deaths were attributable to Duchenne-related causes in patients with known duration of glucocorticoids usage. 28 (9%) deaths occurred in 311 patients treated with glucocorticoids for 1 year or longer compared with 11 (19%) deaths in 58 patients with no history of glucocorticoid use (odds ratio 0·47, 95% CI 0·22–1·00; p=0·0501). In patients with Duchenne muscular dystrophy, glucocorticoid treatment is associated with reduced risk of losing clinically meaningful mobility and upper limb disease progression milestones across the lifespan as well as reduced risk of death. US Department of Education/National Institute on Disability and Rehabilitation Research; US Department of Defense; National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases; and Parent Project Muscular Dystrophy.

All living organisms need energy and metabolic building blocks to sustain biological processes. Wang et al. review immunometabolism, applying the principles of life history theory. They highlight recent advances showing the reciprocal interactions between systemic metabolism and immunity, as well as how inflammation can alter the functional state of metabolic organs and their central control by the hypothalamus. Such coordinated cross-talk between whole-body and immune cell metabolism is involved in a variety of health and disease states. Science , this issue p. eaar3932 Metabolism is at the core of all biological functions. Anabolic metabolism uses building blocks that are either derived from nutrients or synthesized de novo to produce the biological infrastructure, whereas catabolic metabolism generates energy to fuel all biological processes. Distinct metabolic programs are required to support different biological functions. Thus, recent studies have revealed how signals regulating cell quiescence, proliferation, and differentiation also induce the appropriate metabolic programs. In particular, a wealth of new studies in the field of immunometabolism has unveiled many examples of the connection among metabolism, cell fate decisions, and organismal physiology. We discuss these findings under a unifying framework derived from the evolutionary and ecological principles of life history theory.

As cyberattacks become more intelligent, it is challenging to detect advanced attacks in a variety of fields including industry, national defense, and healthcare. Traditional intrusion detection systems are no longer enough to detect these advanced attacks with unexpected patterns. Attackers bypass known signatures and pretend to be normal users. Deep learning is an alternative to solving these issues. Deep Learning (DL)-based intrusion detection does not require a lot of attack signatures or the list of normal behaviors to generate detection rules. DL defines intrusion features by itself through training empirical data. We develop a DL-based intrusion model especially focusing on denial of service (DoS) attacks. For the intrusion dataset, we use KDD CUP 1999 dataset (KDD), the most widely used dataset for the evaluation of intrusion detection systems (IDS). KDD consists of four types of attack categories, such as DoS, user to root (U2R), remote to local (R2L), and probing. Numerous KDD studies have been employing machine learning and classifying the dataset into the four categories or into two categories such as attack and benign. Rather than focusing on the broad categories, we focus on various attacks belonging to same category. Unlike other categories of KDD, the DoS category has enough samples for training each attack. In addition to KDD, we use CSE-CIC-IDS2018 which is the most up-to-date IDS dataset. CSE-CIC-IDS2018 consists of more advanced DoS attacks than that of KDD. In this work, we focus on the DoS category of both datasets and develop a DL model for DoS detection. We develop our model based on a Convolutional Neural Network (CNN) and evaluate its performance through comparison with an Recurrent Neural Network (RNN). Furthermore, we suggest the optimal CNN design for the better performance through numerous experiments.

Tissue-resident immune cells are important for organ homeostasis and defense. The epithelium may contribute to these functions directly or by cross-talk with immune cells. We used single-cell RNA sequencing to resolve the spatiotemporal immune topology of the human kidney. We reveal anatomically defined expression patterns of immune genes within the epithelial compartment, with antimicrobial peptide transcripts evident in pelvic epithelium in the mature, but not fetal, kidney. A network of tissue-resident myeloid and lymphoid immune cells was evident in both fetal and mature kidney, with postnatal acquisition of transcriptional programs that promote infection-defense capabilities. Epithelial-immune cross-talk orchestrated localization of antibacterial macrophages and neutrophils to the regions of the kidney most susceptible to infection. Overall, our study provides a global overview of how the immune landscape of the human kidney is zonated to counter the dominant immunological challenge.