The Influence of Laminate Design on Cell Degradation

AbstractIn this study, the influence of the PV laminate design on the silicon cell degradation was investigated. Laminates consisting of two different kinds of encapsulation (EVA and PVB) and three different back-sheet materials (TAPT, PA and a TPT foils) were manufactured. Standard cells with a two and three bus bar design were used as well as MWT cells. The laminates were subjected to a UV, heat and damp-heat aging tests. The degradation of the cell metallization was investigated by means of electroluminescence imaging, the degree of polymeric aging was determined by Raman spectroscopy. Special attention was paid to the spatial distribution of corrosion effects on the cell. A severe influence of the solar cell type, i.e. the metallization paste, could be shown. Furthermore, a strong dependence of the degree of metallization degradation on the type of back-sheet material was found. An extensive UV aging for up to 180 kWh appeared to have no influence on the metallization corrosion

BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge

Given an audio-visual pair, audio-visual segmentation (AVS) aims to locate sounding sources by predicting pixel-wise maps. Previous methods assume that each sound component in an audio signal always has a visual counterpart in the image. However, this assumption overlooks that off-screen sounds and background noise often contaminate the audio recordings in real-world scenarios. They impose significant challenges on building a consistent semantic mapping between audio and visual signals for AVS models and thus impede precise sound localization. In this work, we propose a two-stage bootstrapping audio-visual segmentation framework by incorporating multi-modal foundation knowledge. In a nutshell, our BAVS is designed to eliminate the interference of background noise or off-screen sounds in segmentation by establishing the audio-visual correspondences in an explicit manner. In the first stage, we employ a segmentation model to localize potential sounding objects from visual data without being affected by contaminated audio signals. Meanwhile, we also utilize a foundation audio classification model to discern audio semantics. Considering the audio tags provided by the audio foundation model are noisy, associating object masks with audio tags is not trivial. Thus, in the second stage, we develop an audio-visual semantic integration strategy (AVIS) to localize the authentic-sounding objects. Here, we construct an audio-visual tree based on the hierarchical correspondence between sounds and object categories. We then examine the label concurrency between the localized objects and classified audio tags by tracing the audio-visual tree. With AVIS, we can effectively segment real-sounding objects. Extensive experiments demonstrate the superiority of our method on AVS datasets, particularly in scenarios involving background noise. Our project website is https://yenanliu.github.io/AVSS.github.io/

Gut stem cell aging is driven by mTORC1 via a p38 MAPK-p53 pathway.

Nutrients are absorbed solely by the intestinal villi. Aging of this organ causes malabsorption and associated illnesses, yet its aging mechanisms remain unclear. Here, we show that aging-caused intestinal villus structural and functional decline is regulated by mTORC1, a sensor of nutrients and growth factors, which is highly activated in intestinal stem and progenitor cells in geriatric mice. These aging phenotypes are recapitulated in intestinal stem cell-specific Tsc1 knockout mice. Mechanistically, mTORC1 activation increases protein synthesis of MKK6 and augments activation of the p38 MAPK-p53 pathway, leading to decreases in the number and activity of intestinal stem cells as well as villus size and density. Targeting p38 MAPK or p53 prevents or rescues ISC and villus aging and nutrient absorption defects. These findings reveal that mTORC1 drives aging by augmenting a prominent stress response pathway in gut stem cells and identify p38 MAPK as an anti-aging target downstream of mTORC1

In-N-Out Generative Learning for Dense Unsupervised Video Segmentation

In this paper, we focus on the unsupervised learning for Video Object Segmentation (VOS) which learns visual correspondence (i.e., similarity between pixel-level features) from unlabeled videos. Previous methods are mainly based on the contrastive learning paradigm, which optimize either in image level or pixel level. Image-level optimization (e.g., the spatially pooled feature of ResNet) learns robust high-level semantics but is sub-optimal since the pixel-level features are optimized implicitly. By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation. To complementarily perform these two levels of optimization in a unified framework, we propose the In-aNd-Out (INO) generative learning from a purely generative perspective with the help of naturally designed class tokens and patch tokens in Vision Transformer (ViT). Specifically, for image-level optimization, we force the out-view imagination from local to global views on class tokens, which helps capturing high-level semantics, and we name it as out-generative learning. As to pixel-level optimization, we perform in-view masked image modeling on patch tokens, which recovers the corrupted parts of an image via inferring its fine-grained structure, and we term it as in-generative learning. To better discover the temporal information, we additionally force the inter-frame consistency from both feature level and affinity matrix level. Extensive experiments on DAVIS-2017 val and YouTube-VOS 2018 val show that our INO outperforms previous state-of-the-art methods by significant margins

Dynamic Gradient Reactivation for Backward Compatible Person Re-identification

We study the backward compatible problem for person re-identification (Re-ID), which aims to constrain the features of an updated new model to be comparable with the existing features from the old model in galleries. Most of the existing works adopt distillation-based methods, which focus on pushing new features to imitate the distribution of the old ones. However, the distillation-based methods are intrinsically sub-optimal since it forces the new feature space to imitate the inferior old feature space. To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features. Different from previous methods, RBCL only pushes the new features to find best-ranking positions in the old feature space instead of strictly alignment, and is in line with the ultimate goal of backward retrieval. However, the sharp sigmoid function used to make the ranking metric differentiable also incurs the gradient vanish issue, therefore stems the ranking refinement during the later period of training. To address this issue, we propose the Dynamic Gradient Reactivation (DGR), which can reactivate the suppressed gradients by adding dynamic computed constant during forward step. To further help targeting the best-ranking positions, we include the Neighbor Context Agents (NCAs) to approximate the entire old feature space during training. Unlike previous works which only test on the in-domain settings, we make the first attempt to introduce the cross-domain settings (including both supervised and unsupervised), which are more meaningful and difficult. The experimental results on all five settings show that the proposed RBCL outperforms previous state-of-the-art methods by large margins under all settings.Comment: Submitted to Pattern Recognition on Dec 06, 2021. Under Revie

Study of the Influence of the Reprocessing Cycles on the Final Properties of Polylactide Pieces Obtained by Injection Molding

[EN] This research work aims to study the influence of the reprocessing cycles on the mechanical, thermal, and thermomechanical properties of polylactide (PLA). To this end, PLA was subjected to as many as six extrusion cycles and the resultant pellets were shaped into pieces by injection molding. Mechanical characterization revealed that the PLA pieces presented relatively similar properties up to the third reprocessing cycle, whereas further cycles induced an intense reduction in ductility and toughness. The effect of the reprocessing cycles was also studied by the changes in the melt fluidity, which showed a significant increase after four reprocessing cycles. An increase in the bio-polyester chain mobility was also attained with the number of the reprocessing cycles that subsequently favored an increase in crystallinity of PLA. A visual inspection indicated that PLA developed certain yellowing and the pieces also became less transparent with the increasing number of reprocessing cycles. Therefore, the obtained results showed that PLA suffers a slight degradation after one or two reprocessing cycles whereas performance impairment becomes more evident above the fourth reprocessing cycle. This finding suggests that the mechanical recycling of PLA for up to three cycles of extrusion and subsequent injection molding is technically feasible.This research was funded by the Spanish Ministry of Science, Innovation, and Universities (MICIU) project numbers MAT2017-84909-C2-2-R and AGL2015-63855-C2-1-R. L. Quiles-Carrillo wants to thank GV for his FPI grant (ACIF/2016/182) and MECD for his FPU grant (FPU15/03812). D. Lascano wants to thank UPV for the grant received through the PAID-01-18 program. S. Torres-Giner is recipient of a Juan de la Cierva¿ Incorporación contract (IJCI-2016-29675) from MICIU. 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Responses of Karenia mikimotoi to allelochemical linoleic acid: Growth inhibition, photosynthetic damage, oxidative stress and cell apoptosis

Linoleic acid (LA), a potentially algae-inhibiting chemical released by macroalgae, has been shown to hinder the growth of numerous bloom-forming species. The allelopathic effects of LA (varying from 100 μg/L to 900 μg/L) on harmful microalgae K. mikimotoi were examined using population growth dynamics and physiological levels of K. mikimotoi. LA (>500 μg/L) strongly inhibited algal growth with most cells halted at the S and G2 phases and an evident drop in photosynthetic pigments (chlorophyll a (chl a), chlorophyll c (chl c) and carotenoids). Furthermore, chlorophyll fluorescence parameters such as Fv/Fm, PI, ETo/RC showed a declining trend whereas ABS/RC, DIo/RC, TRo/RC showed an increasing trend with increasing LA exposure concentrations. The level of intracellular reactive oxygen species (ROS) was considerably higher, indicating that LA promoted oxidative stress in K. mikimotoi. Excessive ROS promoted apoptosis in K. mikimotoi, which was noted by increased activity of caspase-3, caspase-9, and flow cytometry (FCM) data. Furthermore, N-acetylcysteine (NAC) and N-Acetyl-Asp-Glu-Val-Asp-CHO (Ac-DEVD-CHO) lowered the apoptotic rates of the LA-treated algal cells, indicating that the aforementioned inhibitors delayed K. mikimotoi apoptosis under LA treatment. To summarize, cell cycle arrest of K. mikimotoi is less sensitive to ROS, but the overproduction of ROS generated by LA activated caspase-3 and caspase-9, which further promoted the apoptosis of K. mikimotoi. This research showed that LA might have great potential and application prospects in controlling the outbreak of harmful algae

Integrated glycomic analysis of ovarian cancer side population cells

Additional file 2. The category of representative lectins for glycan profiling