COVID-19 spreading patterns in family clusters reveal gender roles in China

Unfolding different gender roles is preceding the efforts to reduce gender inequality. This paper analyzes COVID-19 family clusters outside Hubei Province in mainland China during the 2020 outbreak, revealing significant differences in spreading patterns across gender and family roles. Results show that men are more likely to be the imported cases of a family cluster, and women are more likely to be infected within the family. This finding provides new supportive evidence of the men as breadwinner and women as homemaker (MBWH) gender roles in China. Further analyses reveal that the MBWH pattern is stronger in eastern than in western China, stronger for younger than for elder people. This paper offers not only valuable references for formulating gender-differentiated epidemic prevention policies but also an exemplification for studying group differences in similar scenarios.Comment: 13 pages, 5 figures, 2 table

MotionGPT: Human Motion as a Foreign Language

Though the advancement of pre-trained large language models unfolds, the exploration of building a unified model for language and other multi-modal data, such as motion, remains challenging and untouched so far. Fortunately, human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training that can enhance the performance of motion-related tasks becomes feasible. Driven by this insight, we propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks. Specifically, we employ the discrete vector quantization for human motion and transfer 3D motion into motion tokens, similar to the generation process of word tokens. Building upon this "motion vocabulary", we perform language modeling on both motion and text in a unified manner, treating human motion as a specific language. Moreover, inspired by prompt learning, we pre-train MotionGPT with a mixture of motion-language data and fine-tune it on prompt-based question-and-answer tasks. Extensive experiments demonstrate that MotionGPT achieves state-of-the-art performances on multiple motion tasks including text-driven motion generation, motion captioning, motion prediction, and motion in-between.Comment: Project Page: https://github.com/OpenMotionLab/MotionGP

Understanding the Effect of Sodium Polyphosphate on Improving the Chemical Stability of Ti3c2tz Mxene in Water

Degradation of MXenes in aqueous environments severely limits the application and industrialization of this large family of two-dimensional (2D) materials. Hydrolysis and oxidation are now considered as two main degradation mechanisms and while significant efforts have been directed to prolonging the shelf-life of MXenes, separating and studying their degradation mechanisms have lagged behind. Herein, gas analysis via gas chromatography and Raman spectroscopy were used to investigate the effect of sodium polyphosphate, PP, on the degradation of Ti3C2Tz MXene. Transmission and scanning electron microscopies, as well as X-ray photoelectron spectroscopywere also used as complimentary techniques to support conclusions derived from gas analysis and to confirm the extent of degradation via characterization of solid reaction products. Based on these studies we have determined that the addition of PP to an equal mass of Ti3C2Tz solution can effectively suppress hydrolysis and protect Ti3C2Tz from degradation

A Study on Forming a Construction Land Market That Unifies Urban and Rural Areas During the Process of the New-Type Urbanization in China

Under the background of China’s new-type urbanization, the transformation of urbanization which is people-centered is quietly underway. The new-type urbanization not only puts people first, but also is firmly based on establishing a construction land market that unifies urban and rural areas. The Third Plenary Session of the 18th Central Committee of the Chinese Communist Party adopted the “Decision on Major Issues Concerning Comprehensively Deepening Reforms” (hereinafter referred to as the “Decision”), in which “forming a construction land market that unifies urban and rural areas” was proposed, and the Decision advocated that the rural collectively-owned construction land can “enter the market with the same rights and at the same prices as state-owned land”. This paper firstly reviewed the status quo of China’s construction land market in which there is segmentation between urban and rural areas. Then under the guidance of the specific reforming measures proposed by the Decision, the paper made a further attempt to discuss and explore the supporting measures of and safeguard mechanism for forming a construction land market that unifies urban and rural areas

Executing your Commands via Motion Diffusion in Latent Space

We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities, such as textual descriptors in natural languages, it is hard to learn a probabilistic mapping from the desired conditional modality to the human motion sequences. Besides, the raw motion data from the motion capture system might be redundant in sequences and contain noises; directly modeling the joint distribution over the raw motion sequences and conditional modalities would need a heavy computational overhead and might result in artifacts introduced by the captured noises. To learn a better representation of the various human motion sequences, we first design a powerful Variational AutoEncoder (VAE) and arrive at a representative and low-dimensional latent code for a human motion sequence. Then, instead of using a diffusion model to establish the connections between the raw motion sequences and the conditional inputs, we perform a diffusion process on the motion latent space. Our proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages. Extensive experiments on various human motion generation tasks demonstrate that our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks, with two orders of magnitude faster than previous diffusion models on raw motion sequences.Comment: 18 pages, 11 figures, conferenc

Recent advances in neural mechanism of general anesthesia induced unconsciousness: insights from optogenetics and chemogenetics

For over 170 years, general anesthesia has played a crucial role in clinical practice, yet a comprehensive understanding of the neural mechanisms underlying the induction of unconsciousness by general anesthetics remains elusive. Ongoing research into these mechanisms primarily centers around the brain nuclei and neural circuits associated with sleep-wake. In this context, two sophisticated methodologies, optogenetics and chemogenetics, have emerged as vital tools for recording and modulating the activity of specific neuronal populations or circuits within distinct brain regions. Recent advancements have successfully employed these techniques to investigate the impact of general anesthesia on various brain nuclei and neural pathways. This paper provides an in-depth examination of the use of optogenetic and chemogenetic methodologies in studying the effects of general anesthesia on specific brain nuclei and pathways. Additionally, it discusses in depth the advantages and limitations of these two methodologies, as well as the issues that must be considered for scientific research applications. By shedding light on these facets, this paper serves as a valuable reference for furthering the accurate exploration of the neural mechanisms underlying general anesthesia. It aids researchers and clinicians in effectively evaluating the applicability of these techniques in advancing scientific research and clinical practice

3D Unet-based Kidney and Kidney Tumer Segmentation with Attentive Feature Learning

To study the kidney diseases and kidney tumor from Computed Tomography(CT) imaging data, it is helpful to segment the region of interest through computer aided auto-segmentation tool. In the KiTs 2019 challenge [1], we are provided 3D volumetric CT data to train a model for kidney and kidney tumor segmentation. We introduce an improved deep 3D Unet by enriching the feature representation in CT images using an attention module. We achieve 1.5% improvement in the segmentation accuracy when evaluated on the validation set

Comparison of PET/CT and MRI in the Diagnosis of Bone Metastasis in Prostate Cancer Patients: A Network Analysis of Diagnostic Studies

Background: Accurate diagnosis of bone metastasis status of prostate cancer (PCa) is becoming increasingly more important in guiding local and systemic treatment. Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) have increasingly been utilized globally to assess the bone metastases in PCa. Our meta-analysis was a high-volume series in which the utility of PET/CT with different radioligands was compared to MRI with different parameters in this setting. Materials and Methods: Three databases, including Medline, Embase, and Cochrane Library, were searched to retrieve original trials from their inception to August 31, 2019 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. The methodological quality of the included studies was assessed by two independent investigators utilizing Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A Bayesian network meta-analysis was performed using an arm-based model. Absolute sensitivity and specificity, relative sensitivity and specificity, diagnostic odds ratio (DOR), and superiority index, and their associated 95% confidence intervals (CI) were used to assess the diagnostic value. Results: Forty-five studies with 2,843 patients and 4,263 lesions were identified. Network meta-analysis reveals that 68Ga-labeled prostate membrane antigen (68Ga-PSMA) PET/CT has the highest superiority index (7.30) with the sensitivity of 0.91 and specificity of 0.99, followed by 18F-NaF, 11C-choline, 18F-choline, 18F-fludeoxyglucose (FDG), and 18F-fluciclovine PET/CT. The use of high magnetic field strength, multisequence, diffusion-weighted imaging (DWI), and more imaging planes will increase the diagnostic value of MRI for the detection of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT was performed in the detection of bone metastasis on patient-based level (sensitivity, 0.94 vs. 0.91; specificity, 0.94 vs. 0.96; superiority index, 4.43 vs. 4.56). Conclusions: 68Ga-PSMA PET/CT is recommended for the diagnosis of bone metastasis in prostate cancer patients. Where available, 3.0-T high-quality MRI approaches 68Ga-PSMA PET/CT should be performed in the detection of bone metastasis