‘I feel safer in the streets than at home’: Rethinking harm reduction for women in the urban margins

ABSTRACT

Through qualitative data collected with women affected by drug use and drug-related violence in Bogotá, this article explores the convergence of harm reduction rationales and violence prevention programming in the urban margins to advocate for women’s health empowerment and health rights as victims of intergenerational trauma and violence. We propose a methodological shift of public health praxis from street-based outreach models to intimate spaces of intervention for health outcomes embodiment ¹ 1 Drawing from Krieger’s (2004) conceptualisation, we define embodiment as the stories our bodies hold and how they interact with the urban environment. as we continue to develop our community health model to work with marginalised communities in the urban global South. Through this work committed to social justice in marginalised urban communities, we seek to support women’s health needs through harm reduction in historically marginalised communities in urban settings. Our results expose how multi-level gender-based violence affects women’s health in their living spaces in the urban margins. Drawing from women’s voices and narratives of urban violence, we call for a feminist alternative to traditionally masculinist and public-space oriented harm reduction practice for health empowerment in the urban margins.     

Prodromal Parkinson disease in patients with idiopathic hyposmia

Journal of Neurology - Idiopathic hyposmia (IH) is a prodromal marker of Parkinson disease (PD). However, IH is common in the general population and only a minority will develop PD. Identification...     

An adaptively weighted multi-feature method for object-based change detection in high spatial resolution remote sensing images

ABSTRACT

In this letter, an adaptively weighted multi-feature-based method for unsupervised object-based change detection in high-resolution remote sensing images is proposed. First, a sample selection strategy using fuzzy c-means is designed to obtain high precision pseudo-samples in an unsupervised way. Second, the multiple candidate features are categorized into spectral, geometric and textural groups and two kinds of weights are involved taking into account different contributions. The within-group weights for each feature can be calculated based on single-feature distribution curve without any prior distribution assumption, and the between-group weights for each group are decided by scatter matrices. Third, the weighted multi-feature method is used to generate a reliable difference image which is directly clustered to obtain the final change map. Compared with the other five state-of-the-art methods, the experimental results on two datasets demonstrate the effectiveness and superiority of the proposed method.     

温阳散瘀方联合贝前列素钠片治疗下肢动脉粥样硬化性闭塞症的临床研究

目的观察温阳散瘀方辅助贝前列素钠片治疗下肢动脉粥样硬化性闭塞症的效果。方法采用简单随机抽样方法从2016年3月至2018年4月于该院接受诊治的下肢动脉粥样硬化性闭塞症患者中选取86例纳入研究,依据随机数字表法进行分组,对照组43例患者接受贝前列素钠片治疗,研究组43例患者接受温阳散瘀方联合贝前列素钠片治疗,治疗6周后评价两组临床疗效,测定两组患者血脂水平、踝肱指数(ABI)、6min步行试验(6-MWT)、无痛行走距离、超敏C反应蛋白(hs-CRP)以及血液流变学相关指标。结果研究组临床总有效率(88.37%)明显高于对照组(69.77%),差异有统计学意义(P<0.05);治疗后两组患者总胆固醇(TC)、三酰甘油(TG)、低密度脂蛋白胆固醇(LDL-C)水平均明显下降(P<0.05),且组间比较差异有统计学意义(P<0.05);治疗后研究组ABI、6-MWT、无痛行走距离和hs-CRP水平均明显优于对照组(P<0.05);治疗后两组血液流变学相关指标均明显下降(P<0.05),且组间比较差异有统计学意义(P<0.05)。结论温阳散瘀方辅助贝前列素钠片应用于下肢动脉粥样硬化性闭塞症的临床治疗,在平衡血脂水平、改善血液流变学相关指标方面效果明显,可有效改善ABI、6-MWT、无痛行走距离和hs-CRP等指标,增进疗效。     

AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16 h−1Mpc and the HR halo mass function to within 10% down to 1011 M⊙. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

As telescopes and satellites become more powerful, observational data on galaxies, quasars, and the matter in intergalactic space becomes more detailed and covers a greater range of epochs and environments in the Universe. Our cosmological simulations (see, e.g., ref. 1) must also become more detailed and more wide-ranging in order to make predictions and test the effects of different physical processes and different dark-matter candidates. Even with supercomputers, we are forced to decide whether to maximize either resolution or volume, or else compromise on both. These limitations can be overcome through the development of methods that leverage techniques from the artificial intelligence (AI) revolution (see, e.g., ref. 2) and make superresolution (SR) simulations possible. In the present work, we begin to explore this possibility, combining knowledge and existing superscalable codes for petascale-plus cosmological simulations (3) with machine learning (ML) techniques to effectively create representative volumes of the Universe that incorporate information from higher-resolution models of galaxy formation. Our first attempts, presented here, involve simulations with dark matter and gravity only, and extensions to full hydrodynamics will follow. This hybrid approach, which will imply offloading simulations to neural networks (NNs) and other ML algorithms, has the promise to enable the prediction of quasar, supermassive black hole, and galaxy properties in a way that is statistically identical to full hydrodynamic models, but with a significant speed-up.Adding details to images below the resolution scale (SR image enhancement) has become possible with the latest advances in deep learning (DL; ML with NN; ref. 4), including generative adversarial networks (GANs; ref. 5). The technique has applications in many fields, from microscopy to law enforcement (6). It has been used for observational astronomical images by (7), to recover galaxy features from below the resolution scale in degraded Hubble Space Telescope images. Besides SR image enhancement, DL has started to find applications in cosmological simulations. For example, refs. 8 and 9 showed how NNs can predict the nonlinear formation of structures given simple linear theory predictions. NN models have also been trained to predict galaxies (10, 11) and 21-cm emission from neutral hydrogen (12) from simulations that only contain dark matter. GANs have been used in ref. 13 to generate image slices of cosmological models and to generate dark-matter halos from density fields (14). ML techniques other than DL find many applications, too. For example, Kamdar et al. (15) have applied extremely randomized trees to dark-matter simulations to predict hydrodynamic galaxy properties.Generating mocks for future sky surveys requires large volumes and high accuracy, a task that quickly becomes computationally prohibitive. To alleviate the cost, recently, Dai and Seljak (16) developed a Lagrangian-based parametric ML model to predict various hydrodynamical outputs from the dark-matter density field. In other work, Dai et al. (17, 18) sharpened the particle distribution using a potential gradient descent method starting from low-resolution (LR) simulations. Note, however, that these approaches did not aim to enhance the spatial or mass resolution of a simulation.On the DL side, recently, Ramanah et al. (19) explored using the SR technique to map density fields of LR cosmological simulations to that of the high-resolution (HR) ones. While the goal is similar, our work has the following three key differences. First, instead of focusing on the dark-matter density field, we aim to enhance the number of particles and predict their displacements, from which the density fields can be inferred. This approach allows us to preserve the particle nature of the N-body simulations and therefore to interpret the SR outputs as simulations themselves. Second, we test our technique at a higher SR ratio. Compared to ref. 19, which increased the number of Eulerian voxels by 8 times, we increase the number of particles and thus the mass resolution by a factor of 512. Finally, to facilitate future applications of SR on hydrodynamic simulations in representative volumes, we test our method at much smaller scales and in large simulations whose volume is much bigger than that of the training data.