A cusp catastrophe model of mid–long-term landslide evolution over low latitude highlands of China

AbstractBased on a model describing a certain landslide case and catastrophe theory, we derived a cusp catastrophe model and corresponding inversion method to study mid–long-term landslide evolution. According to data of landslides, precipitation, and socioeconomic development from 1976 to 2008, the cusp catastrophe model describing this landslide evolution across a low-latitude highland area in China is obtained with the least squares method. Results of the model indicate that human activity determines landslide intensity. Local precipitation also impacts yearly landslide intensity to some extent, and controls the time when a strong and abrupt change in landslides occurs. During the period 1976–2008, there was an abrupt decrease of landslide intensity during 1994–1995, and an abrupt increase during 1995–1996. Since then, there have been frequent landslides in the low-latitude highland, with greater intensity. All these factors provide a scientific basis for formulating a contingency plan regarding landslide disasters

Distinct expression and methylation patterns for genes with different fates following a single whole-genome duplication in flowering plants

For most sequenced flowering plants, multiple whole-genome duplications (WGDs) are found. Duplicated genes following WGD often have different fates that can quickly disappear again, be retained for long(er) periods, or subsequently undergo small-scale duplications. However, how different expression, epigenetic regulation, and functional constraints are associated with these different gene fates following a WGD still requires further investigation due to successive WGDs in angiosperms complicating the gene trajectories. In this study, we investigate lotus (Nelumbo nucifera), an angiosperm with a single WGD during the K–pg boundary. Based on improved intraspecific-synteny identification by a chromosome-level assembly, transcriptome, and bisulfite sequencing, we explore not only the fundamental distinctions in genomic features, expression, and methylation patterns of genes with different fates after a WGD but also the factors that shape post-WGD expression divergence and expression bias between duplicates. We found that after a WGD genes that returned to single copies show the highest levels and breadth of expression, gene body methylation, and intron numbers, whereas the long-retained duplicates exhibit the highest degrees of protein–protein interactions and protein lengths and the lowest methylation in gene flanking regions. For those long-retained duplicate pairs, the degree of expression divergence correlates with their sequence divergence, degree in protein–protein interactions, and expression level, whereas their biases in expression level reflecting subgenome dominance are associated with the bias of subgenome fractionation. Overall, our study on the paleopolyploid nature of lotus highlights the impact of different functional constraints on gene fate and duplicate divergence following a single WGD in plant

Closed-Loop Unsupervised Representation Disentanglement with β\beta-VAE Distillation and Diffusion Probabilistic Feedback

Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks. It currently has at least three unresolved core issues: (i) heavy reliance on label annotation and synthetic data -- causing poor generalization on natural scenarios; (ii) heuristic/hand-craft disentangling constraints make it hard to adaptively achieve an optimal training trade-off; (iii) lacking reasonable evaluation metric, especially for the real label-free data. To address these challenges, we propose a \textbf{C}losed-\textbf{L}oop unsupervised representation \textbf{Dis}entanglement approach dubbed \textbf{CL-Dis}. Specifically, we use diffusion-based autoencoder (Diff-AE) as a backbone while resorting to $\beta$-VAE as a co-pilot to extract semantically disentangled representations. The strong generation ability of diffusion model and the good disentanglement ability of VAE model are complementary. To strengthen disentangling, VAE-latent distillation and diffusion-wise feedback are interconnected in a closed-loop system for a further mutual promotion. Then, a self-supervised \textbf{Navigation} strategy is introduced to identify interpretable semantic directions in the disentangled latent space. Finally, a new metric based on content tracking is designed to evaluate the disentanglement effect. Experiments demonstrate the superiority of CL-Dis on applications like real image manipulation and visual analysis

A RFID-Based Monitoring System for Characterization of Perching Behaviors of Individual Poultry

Perching is a natural behavior of poultry. However, it is difficult to distinguish individual birds in a large group in order to relate perching behavior to health condition or productivity. To enable such research, this study developed and validated a radio frequency identification (RFID)-based automated perching monitoring system (APMS) for characterizing individual perching behaviors of group-housed poultry. The APMS consisted of a RFID module, a load cell module, and a round wooden perch. The RFID module was comprised of a high-frequency RFID reader, three customized rectangular antennas, and multiple RFID transponders. The load cell module was comprised of a data acquisition system and two load cells supporting the two ends of the perch. Daily number of perch visits (PV) and perching duration (PD) of individual birds were used to delineate perching behavior. Three identical experimental pens, five hens per pen, were equipped with the monitoring system. Two RFID transponders were attached to each hen (one per leg) and a distinct color was marked on the bird‘s head for video or visual identification. Performance of the APMS was validated by comparing the system outputs with manual observation/labeling over an entire day. Sensitivity and specificity of the system were shown to improve from 97.77% and 99.88%, respectively, when using only the RFID module, to 99.83% and 99.93%, respectively, when incorporating weight information from the load cell module. This study revealed that the APMS has an excellent performance in measuring perching behaviors of individual birds in a group. The APMS offers great potentials for delineating differences in perching behavior among hens with different social status or health conditions in a group setting

Anti-PD-L1/TGF-βR fusion protein (SHR-1701) overcomes disrupted lymphocyte recovery-induced resistance to PD-1/PD-L1 inhibitors in lung cancer

Background Second-generation programmed cell death-protein 1/programmed death-ligand 1 (PD-1/PD-L1) inhibitors, such as bintrafusp alfa (M7824), SHR-1701, and YM101, have been developed to simultaneously block PD-1/PD-L1 and transforming growth factor-beta/transforming growth factor-beta receptor (TGF-β/TGF-βR). Consequently, it is necessary to identify predictive factors of lung cancer patients who are not only resistant to PD-1/PD-L1 inhibitors but also sensitive to bifunctional drugs. The purpose of this study was to search for such predictors. Methods Multivariable Cox regression was used to study the association between the clinical outcome of treatment with PD-1/PD-L1 inhibitors and lymphocyte recovery after lymphopenia in lung cancer patients. Murine CMT167 lung cancer cells were engineered to express the firefly luciferase gene and implanted orthotopically in the lung of syngeneic mice. Bioluminescence imaging, flow cytometry, and immunohistochemistry were employed to determine response to immunotherapy and function of tumor-infiltrating immune cells. Results For lung cancer patients treated with anti-PD-1/PD-L1 antibodies, poor lymphocyte recovery was associated with a shorter progression-free survival (PFS; P < 0.001), an accumulation of regulatory T cells (Tregs), and an elimination of CD8+ T cells in the peripheral blood. Levels of CD8+ T cells and Treg cells were also imbalanced in the tumors and peripheral immune organs of mice with poor lymphocyte recovery after chemotherapy. Moreover, these mice failed to respond to anti-PD-1 antibodies but remained sensitive to the anti-PD-L1/TGF-βR fusion protein (SHR-1701). Consistently, SHR-1701 but not anti-PD-1 antibodies, markedly enhanced IFN-γ production and Ki-67 expression in peripheral CD8+ T cells from patients with impaired lymphocyte recovery. Conclusions Lung cancer patients with poor lymphocyte recovery and suffering from persistent lymphopenia after previous chemotherapy are resistant to anti-PD-1/PD-L1 antibodies but might be sensitive to second-generation agents such as SHR-1701.publishedVersio

MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

Over the past few decades, multimodal emotion recognition has made remarkable progress with the development of deep learning. However, existing technologies are difficult to meet the demand for practical applications. To improve the robustness, we launch a Multimodal Emotion Recognition Challenge (MER 2023) to motivate global researchers to build innovative technologies that can further accelerate and foster research. For this year's challenge, we present three distinct sub-challenges: (1) MER-MULTI, in which participants recognize both discrete and dimensional emotions; (2) MER-NOISE, in which noise is added to test videos for modality robustness evaluation; (3) MER-SEMI, which provides large amounts of unlabeled samples for semi-supervised learning. In this paper, we test a variety of multimodal features and provide a competitive baseline for each sub-challenge. Our system achieves 77.57% on the F1 score and 0.82 on the mean squared error (MSE) for MER-MULTI, 69.82% on the F1 score and 1.12 on MSE for MER-NOISE, and 86.75% on the F1 score for MER-SEMI, respectively. Baseline code is available at https://github.com/zeroQiaoba/MER2023-Baseline