Alleviation of cadmium toxicity by silicon or sulfate supply in plants

Plants growing in Cd-polluted soils can inevitably take up and accumulate Cd, which will affect plant growth and even threat human health after consuming Cd-containing edible organs of plants. In this thesis, effects of Si and sulfate on Cd toxicity were studied. Although Si can decrease Cd accumulation in plants, the underlying mechanisms are still poorly understood. Hydroponic experiments under short- and long-term Cd treatment in the presence and absence of Si showed that exogenous Si supply decreased Cd in wheat plants. After short-term exposure, Si considerably decreased Cd in the apoplastic fluid of roots. Si neither affected gene expression related to Cd uptake and transport nor cell wall properties, whereas Si delayed suberin deposition in roots. We reason that delayed suberization by Si enlarged longitudinal apoplastic space, thereby decreasing Cd concentrations in apoplastic fluid as a `dilution` effect. After long-term Cd exposure, cell wall properties and the expression of genes related to Cd influx and transport were unaffected. Intriguingly, Si up-regulated Cd efflux-related gene expression and enhanced root oxalate exudation, which might contribute to decrease Cd after long-term Cd exposure. Taken together, our results indicate that Si-dependent decrease in root Cd concentrations during short-term Cd exposure helps plants to mitigate Cd toxicity in the long-term. Products of sulfate assimilation, glutathion (GSH) and phytochelatin (PC) are chelators of Cd and can detoxify Cd toxicity. To date, it is still unclear whether excess sulfate supply could alleviate Cd toxicity. We found that sulfate supply reversed detrimental effects of Cd on biomass and oxidative stress, but also increased Cd concentrations in leaves, suggesting that sulfate enhances Cd tolerance in faba bean. We reason that sulfate accelerated Cd accumulation in cell walls of leaves is related to enhanced Cd tolerance

El Impacto del Coronavirus en nuestra Salud Mental

The Coronavirus represents the greatest threat to physical health in modern times. Simultaneously, fear of the unknown and the fear of the very real repercussions of the virus is threatening to impact the mental health of many around the world. To provide insights on the impact of Coronavirus on our mental health, we are constantly monitoring millions of conversations on Twitter each day, and analysing this enormous amount of data by means of psychological models trained with artificial intelligence techniques and deep neural networks.El Coronavirus representa la mayor amenaza para la salud física en tiempos modernos. A su vez, el miedo a lo desconocido y a las repercusiones reales del virus, está amenazando con impactar en la salud mental de las personas alrededor de todo el mundo. Para analizar dicho impacto, estamos monitorizando millones de conversaciones en Twitter en tiempo real, y analizando esta gran cantidad de datos mediante modelos psicológicos entrenados con técnicas de inteligencia artificial y redes neuronales profundas

MatSpectNet: Material Segmentation Network with Domain-Aware and Physically-Constrained Hyperspectral Reconstruction

Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in a material's appearance. Hyperspectral images, which are sets of spectral measurements sampled at multiple wavelengths, theoretically offer distinct information for material identification, as variations in intensity of electromagnetic radiation reflected by a surface depend on the material composition of a scene. However, existing hyperspectral datasets are impoverished regarding the number of images and material categories for the dense material segmentation task, and collecting and annotating hyperspectral images with a spectral camera is prohibitively expensive. To address this, we propose a new model, the MatSpectNet to segment materials with recovered hyperspectral images from RGB images. The network leverages the principles of colour perception in modern cameras to constrain the reconstructed hyperspectral images and employs the domain adaptation method to generalise the hyperspectral reconstruction capability from a spectral recovery dataset to material segmentation datasets. The reconstructed hyperspectral images are further filtered using learned response curves and enhanced with human perception. The performance of MatSpectNet is evaluated on the LMD dataset as well as the OpenSurfaces dataset. Our experiments demonstrate that MatSpectNet attains a 1.60% increase in average pixel accuracy and a 3.42% improvement in mean class accuracy compared with the most recent publication. The project code is attached to the supplementary material and will be published on GitHub.Comment: 7 pages main pape

Solution structure of the second bromodomain of Brd2 and its specific interaction with acetylated histone tails

<p>Abstract</p> <p>Background</p> <p>Brd2 is a transcriptional regulator and belongs to BET family, a less characterized novel class of bromodomain-containing proteins. Brd2 contains two tandem bromodomains (BD1 and BD2, 46% sequence identity) in the N-terminus and a conserved motif named ET (extra C-terminal) domain at the C-terminus that is also present in some other bromodomain proteins. The two bromodomains have been shown to bind the acetylated histone H4 and to be responsible for mitotic retention on chromosomes, which is probably a distinctive feature of BET family proteins. Although the crystal structure of Brd2 BD1 is reported, no structure features have been characterized for Brd2 BD2 and its interaction with acetylated histones.</p> <p>Results</p> <p>Here we report the solution structure of human Brd2 BD2 determined by NMR. Although the overall fold resembles the bromodomains from other proteins, significant differences can be found in loop regions, especially in the ZA loop in which a two amino acids insertion is involved in an uncommon <it>π</it>-helix, termed <it>π</it>D. The helix <it>π</it>D forms a portion of the acetyl-lysine binding site, which could be a structural characteristic of Brd2 BD2 and other BET bromodomains. Unlike Brd2 BD1, BD2 is monomeric in solution. With NMR perturbation studies, we have mapped the H4-AcK12 peptide binding interface on Brd2 BD2 and shown that the binding was with low affinity (2.9 mM) and in fast exchange. Using NMR and mutational analysis, we identified several residues important for the Brd2 BD2-H4-AcK12 peptide interaction and probed the potential mechanism for the specific recognition of acetylated histone codes by Brd2 BD2.</p> <p>Conclusion</p> <p>Brd2 BD2 is monomeric in solution and dynamically interacts with H4-AcK12. The additional secondary elements in the long ZA loop may be a common characteristic of BET bromodomains. Surrounding the ligand-binding cavity, five aspartate residues form a negatively charged collar that serves as a secondary binding site for H4-AcK12. We suggest that Brd2 BD1 and BD2 may possess distinctive roles and cooperate to regulate Brd2 functions. The structure basis of Brd2 BD2 will help to further characterize the functions of Brd2 and its BET members.</p

QS-TTS: Towards Semi-Supervised Text-to-Speech Synthesis via Vector-Quantized Self-Supervised Speech Representation Learning

This paper proposes a novel semi-supervised TTS framework, QS-TTS, to improve TTS quality with lower supervised data requirements via Vector-Quantized Self-Supervised Speech Representation Learning (VQ-S3RL) utilizing more unlabeled speech audio. This framework comprises two VQ-S3R learners: first, the principal learner aims to provide a generative Multi-Stage Multi-Codebook (MSMC) VQ-S3R via the MSMC-VQ-GAN combined with the contrastive S3RL, while decoding it back to the high-quality audio; then, the associate learner further abstracts the MSMC representation into a highly-compact VQ representation through a VQ-VAE. These two generative VQ-S3R learners provide profitable speech representations and pre-trained models for TTS, significantly improving synthesis quality with the lower requirement for supervised data. QS-TTS is evaluated comprehensively under various scenarios via subjective and objective tests in experiments. The results powerfully demonstrate the superior performance of QS-TTS, winning the highest MOS over supervised or semi-supervised baseline TTS approaches, especially in low-resource scenarios. Moreover, comparing various speech representations and transfer learning methods in TTS further validates the notable improvement of the proposed VQ-S3RL to TTS, showing the best audio quality and intelligibility metrics. The trend of slower decay in the synthesis quality of QS-TTS with decreasing supervised data further highlights its lower requirements for supervised data, indicating its great potential in low-resource scenarios

Do Large Language Models Know What They Don't Know?

Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their performance within their existing knowledge. Despite their vast knowledge, LLMs are still limited by the amount of information they can accommodate and comprehend. Therefore, the ability to understand their own limitations on the unknows, referred to as self-knowledge, is of paramount importance. This study aims to evaluate LLMs' self-knowledge by assessing their ability to identify unanswerable or unknowable questions. We introduce an automated methodology to detect uncertainty in the responses of these models, providing a novel measure of their self-knowledge. We further introduce a unique dataset, SelfAware, consisting of unanswerable questions from five diverse categories and their answerable counterparts. Our extensive analysis, involving 20 LLMs including GPT-3, InstructGPT, and LLaMA, discovering an intrinsic capacity for self-knowledge within these models. Moreover, we demonstrate that in-context learning and instruction tuning can further enhance this self-knowledge. Despite this promising insight, our findings also highlight a considerable gap between the capabilities of these models and human proficiency in recognizing the limits of their knowledge.Comment: 10 pages, 9 figures, accepted by Findings of ACL202