Differences in Species Composition of the Soil Seed Banks among Degraded Patches in an Agro-Pastoral Transition Zone in Inner Mongolian Steppe

Degraded grasslands were distributed in patches characterized by fringed sagebrush (Artemisia frigida), narrowleaf stellera (Stellera chamaejasme), shining speargrass (Achnatherum splendens), or white swordflag (Iris lactea) at an agro-pastoral transition zone of the south Inner Mongolian steppe, which have been retrogressive from a Leymus chinensis steppe. A control patch (undegraded) was located close to the four degraded patches. We investigated the size, composition, species richness of soil seed banks, and its relation to the aboveground vegetation. The density of soil seed banks was highest in the white swordflag patch, intermediate in the shining speargrass and undegraded patches and lowest in the fringed sagebrush and narrowleaf stellera patches. The percentage of the persistent seed bank in the undegraded patch was higher than those in the four degraded patches. Similarities between the soil seed bank of the undegraded patch and degraded patches and between soil seed banks and standing vegetation of the undegraded patch were all low. The potential for in situ regeneration of the established vegetation of the undegraded patch from the soil seed bank is low in all of these four patches. We can assume that restoration of these habitats can not rely on seed banks alone

MagicVideo: Efficient Video Generation With Latent Diffusion Models

We present an efficient text-to-video generation framework based on latent diffusion models, termed MagicVideo. Given a text description, MagicVideo can generate photo-realistic video clips with high relevance to the text content. With the proposed efficient latent 3D U-Net design, MagicVideo can generate video clips with 256x256 spatial resolution on a single GPU card, which is 64x faster than the recent video diffusion model (VDM). Unlike previous works that train video generation from scratch in the RGB space, we propose to generate video clips in a low-dimensional latent space. We further utilize all the convolution operator weights of pre-trained text-to-image generative U-Net models for faster training. To achieve this, we introduce two new designs to adapt the U-Net decoder to video data: a framewise lightweight adaptor for the image-to-video distribution adjustment and a directed temporal attention module to capture frame temporal dependencies. The whole generation process is within the low-dimension latent space of a pre-trained variation auto-encoder. We demonstrate that MagicVideo can generate both realistic video content and imaginary content in a photo-realistic style with a trade-off in terms of quality and computational cost. Refer to https://magicvideo.github.io/# for more examples

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Overparametrized Deep Neural Networks (DNNs) often achieve astounding performances, but may potentially result in severe generalization error. Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al. (2020), in which the Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the generalization. Unfortunately, SAM s computational cost is roughly double that of base optimizers, such as Stochastic Gradient Descent (SGD). This paper thus proposes Efficient Sharpness Aware Minimizer (ESAM), which boosts SAM s efficiency at no cost to its generalization performance. ESAM includes two novel and efficient training strategies-StochasticWeight Perturbation and Sharpness-Sensitive Data Selection. In the former, the sharpness measure is approximated by perturbing a stochastically chosen set of weights in each iteration; in the latter, the SAM loss is optimized using only a judiciously selected subset of data that is sensitive to the sharpness. We provide theoretical explanations as to why these strategies perform well. We also show, via extensive experiments on the CIFAR and ImageNet datasets, that ESAM enhances the efficiency over SAM from requiring 100% extra computations to 40% vis-a-vis base optimizers, while test accuracies are preserved or even improved

Trends in sperm quality by computer-assisted sperm analysis of 49,189 men during 2015–2021 in a fertility center from China

BackgroundSperm quality, including semen volume, sperm count, concentration, and total and progressive motility (collectively, “semen parameters”), has declined in the recent decades. Computer-assisted sperm analysis (CASA) provides sperm kinematic parameters, and the temporal trends of which remain unclear. Our objective is to examine the temporal trend of both semen parameters and kinematic parameters in Shanghai, China, in the recent years.MethodsThis retrospective study analyzed semen parameters and kinematic parameters of 49,819 men attending our reproductive center by using CASA during 2015–2021. The total sample was divided into two groups: samples that surpassed the WHO guideline (2010) low reference limits (“above reference limit” group, ARL; n = 24,575) and samples that did not (“below reference limit” group, BRL; n = 24,614). One-way analysis of variance, Kruskal–Wallis test, independent samples t-test, and covariance analysis were used to assess the differences among groups. Year, age, and abstinence time were included in the multiple linear regression model of the ARL group to adjust the confounders and depict the trends in sperm quality.ResultsAmong all the total sample and the ARL and BRL groups, the age of subjects increased in recent years. Semen volume and sperm count showed declined tendency with years in the total sample, the ARL and BRL groups, and the subgroup of age or abstinence time, whereas sperm velocities showed increased tendency with years on the contrary. The multiple linear regression model of the ARL group, adjusting for age and abstinence time, confirmed these trends. Semen volume (β1= −0.162; CI: −0.172, −0.152), sperm count (β1= −9.97; CI: −10.813, −9.128), sperm concentration (β1 = −0.535; CI: −0.772, −0.299), motility (β1 = −1.751; CI: −1.830, −1.672), and progressive motility (β1 = −1.12; CI: −0.201, −0.145) decreased with year, whereas curvilinear line velocity (VCL) (β1 = 3.058; CI: 2.912, 3.203), straight line velocity (VSL) (β1 = 2.075; CI: 1.990, 2.161), and average path velocity (VAP) (β1 = 2.305; CI: 2.224, 2.386) increased over time (all p < 0.001). In addition, VCL, VSL, and VAP significantly declined with age and abstinence time.ConclusionThe semen parameters declined, whereas the kinematic parameters increased over the recent years. We propose that, although sperm count and motility declined over time, sperm motion velocity increased, suggesting a possible compensatory mechanism of male fertility

Table_1_Trends in sperm quality by computer-assisted sperm analysis of 49,189 men during 2015–2021 in a fertility center from China.docx

BackgroundSperm quality, including semen volume, sperm count, concentration, and total and progressive motility (collectively, “semen parameters”), has declined in the recent decades. Computer-assisted sperm analysis (CASA) provides sperm kinematic parameters, and the temporal trends of which remain unclear. Our objective is to examine the temporal trend of both semen parameters and kinematic parameters in Shanghai, China, in the recent years.MethodsThis retrospective study analyzed semen parameters and kinematic parameters of 49,819 men attending our reproductive center by using CASA during 2015–2021. The total sample was divided into two groups: samples that surpassed the WHO guideline (2010) low reference limits (“above reference limit” group, ARL; n = 24,575) and samples that did not (“below reference limit” group, BRL; n = 24,614). One-way analysis of variance, Kruskal–Wallis test, independent samples t-test, and covariance analysis were used to assess the differences among groups. Year, age, and abstinence time were included in the multiple linear regression model of the ARL group to adjust the confounders and depict the trends in sperm quality.ResultsAmong all the total sample and the ARL and BRL groups, the age of subjects increased in recent years. Semen volume and sperm count showed declined tendency with years in the total sample, the ARL and BRL groups, and the subgroup of age or abstinence time, whereas sperm velocities showed increased tendency with years on the contrary. The multiple linear regression model of the ARL group, adjusting for age and abstinence time, confirmed these trends. Semen volume (β1= −0.162; CI: −0.172, −0.152), sperm count (β1= −9.97; CI: −10.813, −9.128), sperm concentration (β1 = −0.535; CI: −0.772, −0.299), motility (β1 = −1.751; CI: −1.830, −1.672), and progressive motility (β1 = −1.12; CI: −0.201, −0.145) decreased with year, whereas curvilinear line velocity (VCL) (β1 = 3.058; CI: 2.912, 3.203), straight line velocity (VSL) (β1 = 2.075; CI: 1.990, 2.161), and average path velocity (VAP) (β1 = 2.305; CI: 2.224, 2.386) increased over time (all p ConclusionThe semen parameters declined, whereas the kinematic parameters increased over the recent years. We propose that, although sperm count and motility declined over time, sperm motion velocity increased, suggesting a possible compensatory mechanism of male fertility.</p

The Fungal Microbiome of the Upper Airway Is Associated With Future Loss of Asthma Control and Exacerbation Among Children With Asthma.

BACKGROUND: Accumulating evidence suggests that the upper airway bacterial microbiota is implicated in asthma inception, severity, and exacerbation. Unlike bacterial microbiota, the role of the upper airway fungal microbiome (mycobiome) in asthma control is poorly understood. RESEARCH QUESTION: What are the upper airway fungal colonization patterns among children with asthma and their relationship with subsequent loss of asthma control and exacerbation of asthma? STUDY DESIGN AND METHODS: The study was coupled with the Step Up Yellow Zone Inhaled Corticosteroids to Prevent Exacerbations (ClinicalTrials.gov Identifier: NCT02066129) clinical trial. The upper airway mycobiome was investigated using Internal transcribed spacer 1 (ITS1) sequencing of nasal blow samples collected from children with asthma when asthma was well controlled (baseline, n = 194) and during early signs of loss of asthma control (yellow zone [YZ], n = 107). RESULTS: At baseline, 499 fungal genera were detected in the upper airway samples, with two commensal fungal species, Malassezia globosa and Malassezia restricta, being most dominant. The relative abundance of Malassezia species varies by age, BMI, and race. Higher relative abundance of M globosa at baseline was associated with lower risk of future YZ episodes (P = .038) and longer time to development of first YZ episode (P = .022). Higher relative abundance of M globosa at YZ episode was associated with lower risk of progression from YZ episode to severe asthma exacerbation (P = .04). The upper airway mycobiome underwent significant changes from baseline to YZ episode, and increased fungal diversity was correlated highly with increased bacterial diversity (ρ = 0.41). INTERPRETATION: The upper airway commensal mycobiome is associated with future asthma control. This work highlights the importance of the mycobiota in asthma control and may contribute to the development of fungi-based markers to predict asthma exacerbation