Graves’ disease as a driver of depression: a mechanistic insight

Graves’ disease (GD) is characterized by diffuse enlargement and overactivity of the thyroid gland, which may be accompanied by other physical symptoms. Among them, depression can dramatically damage patients’ quality of life, yet its prevalence in GD has not received adequate attention. Some studies have established a strong correlation between GD and increased risk of depression, though the data from current study remains limited. The summary of mechanistic insights regarding GD and depression has underpinned possible pathways by which GD contributes to depression. In this review, we first summarized the clinical evidence that supported the increased prevalence of depression by GD. We then concentrated on the mechanistic findings related to the acceleration of depression in the context of GD, as mounting evidence has indicated that GD promotes the development of depression through various mechanisms, including triggering autoimmune responses, inducing hormonal disorders, and influencing the thyroid-gut-microbiome-brain axis. Finally, we briefly presented potential therapeutic approaches to decreasing the risk of depression among patients with GD

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research

Extending the operating region of inductive power transfer systems through dual-side cooperative control

A wide operating region of inductive power transfer systems indicates a stable output against large load and coupling changes. In most of the published works, output regulation is achieved by single-side regulation that utilizes either primary-side or secondary-side power converters for load regulation. However, secondary-side regulation tends to have a relatively narrow operating region due to space and cost considerations. Meanwhile, although primary-side regulation may provide a larger operating region, system performance can be degraded due to the needs of secondary-side information. To overcome the drawbacks of single-side regulation, this article proposes a dual-side cooperative output regulation method implemented by burst-mode pulse density modulation. In the proposed method, dual-side cooperative control is established by sensing primary-side current drops caused by secondary-side burst operations and, hence, requires no communication devices. With the proposed method, the system operating region can be effectively extended without sacrificing steady-state performance. Experimental results are provided in this article to validate the proposed method.Singapore Maritime Institute (SMI)This work was part of the research program of Maritime Research Between Singapore (Singapore Maritime Institute) and Norway (Research Council of Norway) under Project SMI2019-MA-02, which was supported by the Singapore Maritime Institut