Constraining Multi-scale Pairwise Features between Encoder and Decoder Using Contrastive Learning for Unpaired Image-to-Image Translation

Contrastive learning (CL) has shown great potential in image-to-image translation (I2I). Current CL-based I2I methods usually re-exploit the encoder of the generator to maximize the mutual information between the input and generated images, which does not exert an active effect on the decoder part. In addition, though negative samples play a crucial role in CL, most existing methods adopt a random sampling strategy, which may be less effective. In this paper, we rethink the CL paradigm in the unpaired I2I tasks from two perspectives and propose a new one-sided image translation framework called EnCo. First, we present an explicit constraint on the multi-scale pairwise features between the encoder and decoder of the generator to guarantee the semantic consistency of the input and generated images. Second, we propose a discriminative attention-guided negative sampling strategy to replace the random negative sampling, which significantly improves the performance of the generative model with an almost negligible computational overhead. Compared with existing methods, EnCo acts more effective and efficient. Extensive experiments on several popular I2I datasets demonstrate the effectiveness and advantages of our proposed approach, and we achieve several state-of-the-art compared to previous methods.Comment: 16 pages, 10 figure

MD-Dose: A Diffusion Model based on the Mamba for Radiotherapy Dose Prediction

Radiation therapy is crucial in cancer treatment. Experienced experts typically iteratively generate high-quality dose distribution maps, forming the basis for excellent radiation therapy plans. Therefore, automated prediction of dose distribution maps is significant in expediting the treatment process and providing a better starting point for developing radiation therapy plans. With the remarkable results of diffusion models in predicting high-frequency regions of dose distribution maps, dose prediction methods based on diffusion models have been extensively studied. However, existing methods mainly utilize CNNs or Transformers as denoising networks. CNNs lack the capture of global receptive fields, resulting in suboptimal prediction performance. Transformers excel in global modeling but face quadratic complexity with image size, resulting in significant computational overhead. To tackle these challenges, we introduce a novel diffusion model, MD-Dose, based on the Mamba architecture for predicting radiation therapy dose distribution in thoracic cancer patients. In the forward process, MD-Dose adds Gaussian noise to dose distribution maps to obtain pure noise images. In the backward process, MD-Dose utilizes a noise predictor based on the Mamba to predict the noise, ultimately outputting the dose distribution maps. Furthermore, We develop a Mamba encoder to extract structural information and integrate it into the noise predictor for localizing dose regions in the planning target volume (PTV) and organs at risk (OARs). Through extensive experiments on a dataset of 300 thoracic tumor patients, we showcase the superiority of MD-Dose in various metrics and time consumption

Case report: Shingles-associated probable Bickerstaff brainstem encephalitis with IgM anti-sulfatide positivity

BackgroundBickerstaff brainstem encephalitis (BBE) is a rare disease considered caused by acute demyelination of the brainstem, most often resulting from secondary autoimmune responses. To our knowledge, this is the first probable case report of shingles-associated BBE with anti-sulfatide IgM positivity.Case presentationWe report the case of an 83-year-old woman with symptoms of progressive limb weakness, difficulty swallowing food, and disturbed consciousness that occurred 4 weeks following herpes zoster infection. Autoimmune anti-sulfatide antibodies were positive and fluid-attenuated inversion recovery (FLAIR) sequences revealed clear high signal intensity in pons and bilateral thalamus. Our patient’s condition improved markedly with glucocorticoid treatment. After 2 months of treatment, our patient was fully recovered. We considered that for her case, BBE is the most appropriate diagnosis.ConclusionsWe emphasize the importance of a careful medical history and assessment of clinical symptoms, performing MRI, testing autoimmune antibodies for rapid diagnosis, and ruling out differential diagnoses. Further studies involving more patients with BBE with IgM anti-sulfatide autoantibodies will increase the understanding of the clinical characteristics and advance the diagnosis and treatment of this syndrome. Meanwhile, it is crucial for dermatologists to know about this severe neurological complication following shingles

An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling

Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar reconstructive objectives on video inputs (e.g., masked frame modeling) have been explored in video-language (VidL) pre-training, previous studies fail to find a truly effective MVM strategy that can largely benefit the downstream performance. In this work, we systematically examine the potential of MVM in the context of VidL learning. Specifically, we base our study on a fully end-to-end VIdeO-LanguagE Transformer (VIOLET), where the supervision from MVM training can be backpropagated to the video pixel space. In total, eight different reconstructive targets of MVM are explored, from low-level pixel values and oriented gradients to high-level depth maps, optical flow, discrete visual tokens, and latent visual features. We conduct comprehensive experiments and provide insights into the factors leading to effective MVM training, resulting in an enhanced model VIOLETv2. Empirically, we show VIOLETv2 pre-trained with MVM objective achieves notable improvements on 13 VidL benchmarks, ranging from video question answering, video captioning, to text-to-video retrieval.Comment: CVPR'23; the first two authors contributed equally; code is available at https://github.com/tsujuifu/pytorch_empirical-mv

Broadband Radio Spectral Observations of Solar Eclipse on 2008-08-01 and Implications on the Quiet Sun Atmospheric Model

Based on the joint-observations of the radio broadband spectral emissions of solar eclipse on August 1, 2008 at Jiuquan (total eclipse) and Huairou (partial eclipse) at the frequencies of 2.00 -- 5.60 GHz (Jiuquan), 2.60 -- 3.80 GHZ (Chinese solar broadband radiospectrometer, SBRS/Huairou), and 5.20 -- 7.60 GHz (SBRS/Huairou), the authors assemble a successive series of broadband spectrum with a frequency of 2.60 -- 7.60 GHz to observe the solar eclipse synchronously. This is the first attempt to analyze the solar eclipse radio emission under the two telescopes located at different places with broadband frequencies in the periods of total and partial eclipse. With these analyses, the authors made a new semiempirical model of the coronal plasma density of the quiet Sun and made a comparison with the classic models.Comment: 10 pages, 4 figures, published on Sci. China Ser. G, 2009, Vol.52, page 1765-177

Atmospheric conditions and composition that influence PM2.5 oxidative potential in Beijing, China

Epidemiological studies have consistently linked exposure to PM2.5 with adverse health effects. The oxidative potential (OP) of aerosol particles has been widely suggested as a measure of their potential toxicity. Several acellular chemical assays are now readily employed to measure OP; however, uncertainty remains regarding the atmospheric conditions and specific chemical components of PM2.5 that drive OP. A limited number of studies have simultaneously utilised multiple OP assays with a wide range of concurrent measurements and investigated the seasonality of PM2.5 OP. In this work, filter samples were collected in winter 2016 and summer 2017 during the atmospheric pollution and human health in a Chinese megacity campaign (APHH-Beijing), and PM2.5 OP was analysed using four acellular methods: ascorbic acid (AA), dithiothreitol (DTT), 2,7-dichlorofluorescin/hydrogen peroxidase (DCFH) and electron paramagnetic resonance spectroscopy (EPR). Each assay reflects different oxidising properties of PM2.5, including particle-bound reactive oxygen species (DCFH), superoxide radical production (EPR) and catalytic redox chemistry (DTT/AA), and a combination of these four assays provided a detailed overall picture of the oxidising properties of PM2.5 at a central site in Beijing. Positive correlations of OP (normalised per volume of air) of all four assays with overall PM2.5 mass were observed, with stronger correlations in winter compared to summer. In contrast, when OP assay values were normalised for particle mass, days with higher PM2.5 mass concentrations (µgm−3) were found to have lower mass-normalised OP values as measured by AA and DTT. This finding supports that total PM2.5 mass concentrations alone may not always be the best indicator for particle toxicity. Univariate analysis of OP values and an extensive range of additional measurements, 107 in total, including PM2.5 composition, gas-phase composition and meteorological data, provided detailed insight into the chemical components and atmospheric processes that determine PM2.5 OP variability. Multivariate statistical analyses highlighted associations of OP assay responses with varying chemical components in PM2.5 for both mass- and volume-normalised data. AA and DTT assays were well predicted by a small set of measurements in multiple linear regression (MLR) models and indicated fossil fuel combustion, vehicle emissions and biogenic secondary organic aerosol (SOA) as influential particle sources in the assay response. Mass MLR models of OP associated with compositional source profiles predicted OP almost as well as volume MLR models, illustrating the influence of mass composition on both particle-level OP and total volume OP. Univariate and multivariate analysis showed that different assays cover different chemical spaces, and through comparison of mass- and volume-normalised data we demonstrate that mass-normalised OP provides a more nuanced picture of compositional drivers and sources of OP compared to volume-normalised analysis. This study constitutes one of the most extensive and comprehensive composition datasets currently available and provides a unique opportunity to explore chemical variations in PM2.5 and how they affect both PM2.5 OP and the concentrations of particle-bound reactive oxygen species

Friction Torque Analysis of Planetary Roller Screw Mechanism in Roller Jamming

The load distribution model of the planetary roller screw mechanism (PRSM) is established on the basis of Hertz contact theory. The objective is to obtain a friction torque model of the PRSM in roller jamming. An example is provided to calculate the friction torque of the PRSM in roller jamming. Thereafter, the transmission efficiency is calculated. A static structural analysis is performed using the finite element method to estimate the contact stress between the threads of the PRSM components. Computational results indirectly reveal that roller jamming exerts considerable influence on the friction torque of the PRSM. Results show that the friction torque of the planetary roller screw increases when the roller is jammed and the wear of the parts is accelerated. This condition leads to structural failure. The results of this study can serve as a foundation for electromechanical actuation systems, which can be useful in designing antijamming systems for safety-critical aircraft applications

Objective evaluation of acupuncture treatment in patients with cervical spondylosis by pulse diagnosis device

To study changes in pulse diagram parameters (PDP) in patients with cervical spondylosis (CS) before and after acupuncture treatment, explore the characteristics of PDP and the relationship between PDP changes and therapeutic effectiveness, and provide evidence for outcome prediction and objective evaluation of CS treatment before and after acupuncture treatment.Patients with CS were treated with acupuncture and measured with a pulse acquisition device based on image (PADBI) before the first and after the tenth acupuncture sessions. PDP changes from before until after the acupuncture sessions and patient impressions were analyzed to judge the effect of acupuncture treatment.The PDP values in effective patients were closer to normal values. This indicated that Qi stagnation and blood stasis of the patients were improved. The PDP changes from before to after the first acupuncture treatment were more obvious than those from before to after the tenth acupuncture treatment. This result indicates that the speed of symptom improvement decreased significantly after several acupuncture courses. Analysis of correlation between efficacy and PDP showed that the changes in PDP in five patients was abnormal, which mainly manifested as values of h1, u, p, Pp, and t1, and no significant changes or differences were increased with standard values. This indicated that the symptoms of CS were not improved in these patients.PADBI can provide evidence for outcome prediction of acupuncture treatment in patients with CS. PADBI can provide evidence for objective evaluation of acupuncture treatment of CS