The Impact Of The Accuracy Of Hiv Risk Perception On Hiv Risk Behavior Changes Among Women With Substance Use Disorders In Treatment.

Studies involving women or women with SUDs for PrEP are limited even though in many areas of the world women remain at high-risk for HIV acquisition. This study is to evaluate the impact of accuracy of HIV risk perception on HIV risk behaviors changes over time among women with substance use disorders in treatment, including sexual and injecting risk behaviors. This is a secondary analysis based on a preference controlled un-blinded study. This study enrolled 165 cis- or trans-female volunteers ≥18 years old who were self-reported HIV-uninfected, had diagnosed SUDs, and were presenting for or currently enrolled in drug treatment. 50.6% of participants (N=83) were categorized as underestimating their HIV risk while 49.4% (N=81) were categorized as accurately/over-estimating their HIV risk at baseline. We observed a positive association between underestimating HIV risk at baseline and reduction of HIV risk behaviors over time. Though women who underestimated their HIV risk did reduce their HIV risk behaviors to some extent over time, their HIV risk was still higher than women who accurately or overestimated HIV risk at each subsequent visit. Some high-risk behaviors persisted. The greatest impact of underestimating personal HIV risk on the self-reported HIV risk behaviors was initially after baseline with reduced behavioral change over time, indicating this impact on the change of HIV risk behaviors may be short-lived and fade away if no other intervention is delivered. Though there was a sharp decrease in condomless sex over time among the “underestimate” group, the proportion was above 50% throughout the period of observation, which may be attributed to other determinants that affect women’s decisions of whether to use condom, such as inability to negotiate with sexual partners. Studies collecting more detailed HIV risk behaviors information and considering about other confounders, such as IPV, are needed to research the intensity and duration of the effect of underestimating HIV risk on behavior changes

Predicting and understanding spatio-temporal dynamics of species recovery : implications for Asian crested ibis Nipponia nippon conservation in China

Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 31372218) and cofunded by the China Scholarship Council (CSC) and the ITC Research Fund, Enschede, the Netherlands. We thank Shaanxi Hanzhong Crested Ibis National Nature Reserve for sharing the data of nest site locations. We are grateful to Brendan Wintle, Justin Travis and two anonymous reviewers for helpful comments on a previous version of the manuscript.Peer reviewedPublisher PD

Deep Learning for Semantic Segmentation of UAV Videos

As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention. Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application. Previous methods extended single image segmentation approaches to multiple frames. The temporal dependencies are ignored in these methods. This paper proposes a novel segmentation method to solve this problem. Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately. FCN serves as the frame-based segmentation method. Conv-LSTM makes use of the temporal information between consecutive frames. Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV.</p

Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning

Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint self-supervised pre-training, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data. Unlike joint self-supervised learning, MedCoSS assigns different modality data to different training stages, forming a multi-stage pre-training process. To balance modal conflicts and prevent catastrophic forgetting, we propose a rehearsal-based continual learning method. We introduce the k-means sampling strategy to retain data from previous modalities and rehearse it when learning new modalities. Instead of executing the pretext task on buffer data, a feature distillation strategy and an intra-modal mixup strategy are applied to these data for knowledge retention. We conduct continuous self-supervised pre-training on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT scans, MRI scans, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across nine downstream datasets and its significant scalability in integrating new modality data. Code and pre-trained weight are available at https://github.com/yeerwen/MedCoSS

A Potential Mechanism for Diabetic Wound Healing: Cutaneous Environmental Disorders

Diabetes mellitus is a chronic multi-organ metabolic disorder caused by a combination of environmental and genetic factors. Diabetic complications are considered to be multifactorial with increasing evidence that one of the major pathways involved in the progression of both microvascular and macrovascular diseases is the biochemical process of advanced glycation

Multiband superconductivity and a deep gap minimum evidenced by specific heat in KCa2_2(Fe1−x_{1-x}Nix_x)4_4As4_4F2_2

Specific heat can explore low-energy quasiparticle excitations of superconductors, so it is a powerful tool for bulk measurement on the superconducting gap structure and pairing symmetry. Here, we report an in-depth investigation on the specific heat of the multiband superconductors KCa$_2$(Fe$_{1-x}$Ni$_x$)$_4$As$_4$F$_2$ ($x$ = 0, 0.05, 0.13) single crystals and the overdoped non-superconducting one with $x$ = 0.17. For the samples with $x$ = 0 and $x$ = 0.05, the magnetic field induced specific heat coefficient $\Delta\gamma(H)$ in the low temperature limit increases rapidly below 2 T, then it rises slowly above 2 T. Using the non-superconducting sample with $x$ = 0.17 as a reference, and applying a mixed model that combines Debye and Einstein modes, the specific heat of phonon background for various superconducting samples can be fitted and the detailed information of the electronic specific heat is obtained. Through comparative analyses, it is found that the energy gap structure including two $s$-wave gaps and an extended $s$-wave gap with large anisotropy can reasonably describe the electronic specific heat data. According to these results, we suggest that at least one anisotropic superconducting gap with a deep gap minimum should exist in this multiband system. With the doping of Ni, the $T_c$ of the sample decreases along with the decrease of the large $s$-wave gap, but the extended $s$-wave gap increases due to the enlarged electron pockets via adding more electrons. Despite these changes, the general properties of the gap structure remain unchanged versus doping Ni. In addition, the calculation of condensation energy of the parent and doped samples shows the rough consistency with the correlation of $U_0 \propto {T_c}^n$ with $n$ = 3-4, which is beyond the understanding of the BCS theory