Improving the efficiency of Calibration in Earth System Modelling with Short-term Simulations

Calibration is an important process in developing Earth system models to reduce the deviations between model and observations and improve model performance. Current calibration process is quite resource demanding, requiring enormous computing resources to explore model responses to different parameter choices. In this study, we explored a method using short-term simulations for training emulators to predict model responses given parameter changes in calibration to potentially improve its efficiency. We generated two Perturbed Parameter Ensembles (PPEs) using the same model configuration and perturbed parameters values, but differ in the length of simulation. A significant linear relationship between short and long-term simulations in model response to parameters was found through EOF analysis. The contributions of each ensemble member in respond to parameter values were very similar between PPEs ran for short and long-term, which provided a support for using short-term simulations for emulator in calibration. The evaluation of Gaussian Process emulator trained with short-term simulations showed that the emulator trained with short-term simulations could capture major spatial variability in long-term simulations and generally has a good performance in predicting model responses to parameters. Nevertheless, there are more fragmented and chaotic components in model responses in short-term simulations, which may be caused by the larger internal variability in a shorter length of simulation. Such characteristics in short-term simulations may lead to regional biases when assess model responses in regional scales or related to natural variations using emulators trained with short-term simulations. Given that short-term simulations require much less computational time to run, these results implied that it is possible to use short-term simulations to improve the efficiency of calibration in the Earth system modelling, while the potential regional biases should be aware of when assessing model responses on regional bases

Understanding Client Reactions in Online Mental Health Counseling

Communication success relies heavily on reading participants' reactions. Such feedback is especially important for mental health counselors, who must carefully consider the client's progress and adjust their approach accordingly. However, previous NLP research on counseling has mainly focused on studying counselors' intervention strategies rather than their clients' reactions to the intervention. This work aims to fill this gap by developing a theoretically grounded annotation framework that encompasses counselors' strategies and client reaction behaviors. The framework has been tested against a large-scale, high-quality text-based counseling dataset we collected over the past two years from an online welfare counseling platform. Our study shows how clients react to counselors' strategies, how such reactions affect the final counseling outcomes, and how counselors can adjust their strategies in response to these reactions. We also demonstrate that this study can help counselors automatically predict their clients' states.Comment: Accept to ACL 2023, oral. For code and data, see https://github.com/dll-wu/Client-Reac

PsyBench: a balanced and in-depth Psychological Chinese Evaluation Benchmark for Foundation Models

As Large Language Models (LLMs) are becoming prevalent in various fields, there is an urgent need for improved NLP benchmarks that encompass all the necessary knowledge of individual discipline. Many contemporary benchmarks for foundational models emphasize a broad range of subjects but often fall short in presenting all the critical subjects and encompassing necessary professional knowledge of them. This shortfall has led to skewed results, given that LLMs exhibit varying performance across different subjects and knowledge areas. To address this issue, we present psybench, the first comprehensive Chinese evaluation suite that covers all the necessary knowledge required for graduate entrance exams. psybench offers a deep evaluation of a model's strengths and weaknesses in psychology through multiple-choice questions. Our findings show significant differences in performance across different sections of a subject, highlighting the risk of skewed results when the knowledge in test sets is not balanced. Notably, only the ChatGPT model reaches an average accuracy above $70\%$, indicating that there is still plenty of room for improvement. We expect that psybench will help to conduct thorough evaluations of base models' strengths and weaknesses and assist in practical application in the field of psychology

CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation

Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can be tested in simulation. Sensor simulation involves modeling traffic participants, such as vehicles, with high quality appearance and articulated geometry, and rendering them in real time. The self-driving industry has typically employed artists to build these assets. However, this is expensive, slow, and may not reflect reality. Instead, reconstructing assets automatically from sensor data collected in the wild would provide a better path to generating a diverse and large set with good real-world coverage. Nevertheless, current reconstruction approaches struggle on in-the-wild sensor data, due to its sparsity and noise. To tackle these issues, we present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry, including articulated wheels, with high-quality appearance. Our experiments show our method recovers more accurate shapes from sparse data compared to existing approaches. Importantly, it also trains and renders efficiently. We demonstrate our reconstructed vehicles in several applications, including accurate testing of autonomy perception systems.Comment: CoRL 2022. Project page: https://waabi.ai/cadsim

Research progress of Claudin-low breast cancer

Claudin-low breast cancer (CLBC) is a subgroup of breast cancer discovered at the molecular level in 2007. Claudin is one of the primary proteins that make up tight junctions, and it plays crucial roles in anti-inflammatory and antitumor responses as well as the maintenance of water and electrolyte balance. Decreased expression of claudin results in the disruption of tight junction structures and the activation of downstream signaling pathways, which can lead to tumor formation. The origin of Claudin-low breast cancer is still in dispute. Claudin-low breast cancer is characterized by low expression of Claudin3, 4, 7, E-cadherin, and HER2 and high expression of Vimentin, Snai 1/2, Twist 1/2, Zeb 1/2, and ALDH1, as well as stem cell characteristics. The clinical onset of claudin-low breast cancer is at menopause age, and its histological grade is higher. This subtype of breast cancer is more likely to spread to lymph nodes than other subtypes. Claudin-low breast cancer is frequently accompanied by increased invasiveness and a poor prognosis. According to a clinical retrospective analysis, claudin-low breast cancer can achieve low pathological complete remission. At present, although several therapeutic targets of claudin-low breast cancer have been identified, the effective treatment remains in basic research stages, and no animal studies or clinical trials have been designed. The origin, molecular biological characteristics, pathological characteristics, treatment, and prognosis of CLBC are extensively discussed in this article. This will contribute to a comprehensive understanding of CLBC and serve as the foundation for the individualization of breast cancer treatment

The Lipid Kinase PIP5K1C Regulates Pain Signaling and Sensitization

SummaryNumerous pain-producing (pronociceptive) receptors signal via phosphatidylinositol 4,5-bisphosphate (PIP2) hydrolysis. However, it is currently unknown which lipid kinases generate PIP2 in nociceptive dorsal root ganglia (DRG) neurons and if these kinases regulate pronociceptive receptor signaling. Here, we found that phosphatidylinositol 4-phosphate 5 kinase type 1C (PIP5K1C) is expressed at higher levels than any other PIP5K and, based on experiments with Pip5k1c+/− mice, generates at least half of all PIP2 in DRG neurons. Additionally, Pip5k1c haploinsufficiency reduces pronociceptive receptor signaling and TRPV1 sensitization in DRG neurons as well as thermal and mechanical hypersensitivity in mouse models of chronic pain. We identified a small molecule inhibitor of PIP5K1C (UNC3230) in a high-throughput screen. UNC3230 lowered PIP2 levels in DRG neurons and attenuated hypersensitivity when administered intrathecally or into the hindpaw. Our studies reveal that PIP5K1C regulates PIP2-dependent nociceptive signaling and suggest that PIP5K1C is a therapeutic target for chronic pain

Discovery of a Selective, Substrate-Competitive Inhibitor of the Lysine Methyltransferase SETD8

The lysine methyltransferase SETD8 is the only known methyltransferase that catalyzes monomethylation of histone H4 lysine 20 (H4K20). Monomethylation of H4K20 has been implicated in regulating diverse biological processes including the DNA damage response. In addition to H4K20, SETD8 monomethylates non-histone substrates including proliferating cell nuclear antigen (PCNA) and promotes carcinogenesis by deregulating PCNA expression. However, selective inhibitors of SETD8 are scarce. The only known selective inhibitor of SETD8 to date is nahuoic acid A, a marine natural product, which is competitive with the cofactor. Here, we report the discovery of the first substrate-competitive inhibitor of SETD8, UNC0379 (1). This small-molecule inhibitor is active in multiple biochemical assays. Its affinity to SETD8 was confirmed by ITC (isothermal titration calorimetry) and SPR (surface plasmon resonance) studies. Importantly, compound 1 is selective for SETD8 over 15 other methyltransferases. We also describe structure–activity relationships (SAR) of this series

Transcriptome analysis of osmotic-responsive genes in ABA-dependent and -independent pathways in wheat (Triticum aestivum L.) roots

Bread wheat is one of the most important crops in the world. However, osmotic stress significantly inhibits wheat growth and development, and reduces crop yield and quality. Plants respond to osmotic stress mainly through abscisic acid (ABA)-dependent and -independent pathways. In this study, root transcriptome profiles of wheat seedlings exposed to osmotic stress and exogenous ABA were analysed to identify osmotic-responsive genes belonging to the ABA-dependent or -independent pathways. We found that osmotic stress promoted proline biosynthesis in the ABA-dependent pathway, and trehalose biosynthesis is likely promoted among soluble sugars to maintain protein bioactivity under osmotic stress. In wheat roots subjected to osmotic stress, calcium ions, and glutathione exert their functions mainly through calcium-binding protein (CaM/CML) and glutathione-S-transferase, respectively, depending on both pathways. In addition, a complex relationship among phytohormones signal transduction was observed in response to osmotic stress. The findings of this study deepen our understanding of the molecular mechanisms of osmotic-stress resistance, and provide several candidate osmotic-responsive genes for further study

Exploiting an Allosteric Binding Site of PRMT3 Yields Potent and Selective Inhibitors

Protein arginine methyltransferases (PRMTs) play an important role in diverse biological processes. Among the nine known human PRMTs, PRMT3 has been implicated in ribosomal biosynthesis via asymmetric dimethylation of the 40S ribosomal protein S2 and in cancer via interaction with the DAL-1 tumor suppressor protein. However, few selective inhibitors of PRMTs have been discovered. We recently disclosed the first selective PRMT3 inhibitor, which occupies a novel allosteric binding site and is noncompetitive with both the peptide substrate and cofactor. Here we report comprehensive structure-activity relationship studies of this series, which resulted in the discovery of multiple PRMT3 inhibitors with submicromolar potencies. An X-ray crystal structure of compound 14u in complex with PRMT3 confirmed that this inhibitor occupied the same allosteric binding site as our initial lead compound. These studies provide the first experimental evidence that potent and selective inhibitors can be created by exploiting the allosteric binding site of PRMT3