Adipose Tissue-Resident Immune Cells in Obesity and Type 2 Diabetes

Inflammation is an important contributor to the pathogenesis of obesity-related type 2 diabetes (T2D). Adipose tissue-resident immune cells have been observed, and the potential contribution of these cells to metabolic dysfunction has been appreciated in recent years. This review focused on adipose tissue-resident immune cells that are dysregulated in the context of obesity and T2D. We comprehensively overviewed emerging knowledge regarding the phenotypic and functional properties of these cells and local factors that control their development. We discussed their function in controlling the immune response cascade and disease progression. We also characterized the metabolic profiles of these cells to explain the functional consequences in obese adipose tissues. Finally, we discussed the potential therapeutic targeting of adipose tissue-resident immune cells with the aim of addressing novel therapeutic approaches for the treatment of this disease

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark

Chemical stability of carbon pool in peatlands dominated by different plant types in Jilin province (China) and its potential influencing factors

IntroductionThe peat carbon pool stores 30% of the total global soil carbon accounting for 3–4% of the global land surface. The stability of the peatland carbon pool is a key factor affecting global carbon cycling that is seriously disturbed by climate change and regional human activities. However, the impact of these factors on carbon pool stability remains poorly understood.MethodsBased on the physicochemical properties and carbon compounds of 973 peat samples from Jilin Province (China), which are widely distributed in different altitude regions of the Changbai Mountains, we investigated the stability of the carbon pool in different dominant plants and degradation types of peatlands and assessed the effects of regional environmental factors on the peatland carbon pool.Results and DiscussionOur results showed that the carbohydrate content of peat soils in different peatland types ranged from 33.2 ± 6.9% to 40.5 ± 4.8%, and the aromatic content ranged from 19.8 ± 1.2% to 22.7 ± 2.3%. Bulk density is the most important physicochemical factor, and annual average temperature is the most important environmental factor that influences carbon stability. The effects of selected environmental factors on the peatland carbon pool covered by different plants were different, and the carbon stability in shrub peatlands is more sensitive to climate characteristics than in peatlands dominated by the other two plant types. Peatland degradation decreases the carbon stability in herb and herb/shrub peatlands and increases the carbon stability in shrub peatlands, leading the peatland carbon pool to be more easily influenced by regional human activities than natural peatlands

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