TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. This paper introduces a topologically robust physics-informed tracking framework (TROPHY) for TC tracking. The main idea is to integrate physical knowledge of TC to drastically improve the computational efficiency of multilevel robustness framework for large-scale climate datasets. First, during preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability, thus preserving good candidates for TC tracking. Second, during in-processing, we impose constraints during the multilevel robustness computation to focus only on physics-informed neighborhoods of TCs. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks. In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields

Soft Language Clustering for Multilingual Model Pre-training

Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from source languages or when pre-training data is limited in size. In this paper, we propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods. On the tasks of XTREME including text classification, sequence labeling, question answering, and sentence retrieval, both base- and large-size language models pre-trained with our proposed method exhibit consistent performance improvement. Furthermore, it provides substantial advantages for low-resource languages in unsupervised sentence retrieval and for target languages that differ greatly from the source language in cross-lingual transfer

Genetic etiological analysis of auditory neuropathy spectrum disorder by next-generation sequencing

ObjectiveAuditory neuropathy spectrum disease (ANSD) is caused by both environmental and genetic causes and is defined by a failure in peripheral auditory neural transmission but normal outer hair cells function. To date, 13 genes identified as potentially causing ANSD have been documented. To study the etiology of ANSD, we collected 9 probands with ANSD diagnosed in the clinic and performed targeted next-generation sequencing.MethodsNine probands have been identified as ANSD based on the results of the ABR tests and DPOAE/CMs. Genomic DNA extracted from their peripheral blood was examined by next-generation sequencing (NGS) for a gene panel to identify any potential causal variations. For candidate pathogenic genes, we performed co-segregation among all family members of the pedigrees. Subsequently, using a mini-gene assay, we examined the function of a novel splice site mutant of OTOF.ResultsWe analyzed nine cases of patients with ANSD with normal CMs/DPOAE and abnormal ABR, discovered three novel mutants of the OTOF gene that are known to cause ANSD, and six cases of other gene mutations including TBC1D24, LARS2, TIMM8A, MITF, and WFS1.ConclusionOur results extend the mutation spectrum of the OTOF gene and indicate that the genetic etiology of ANSD may be related to gene mutations of TBC1D24, LARS2, TIMM8A, MITF, and WFS1

Arbitrary Few Parameters are Good Enough for Adapting Large-scale Pre-trained Language Models

Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by only training minimal parameters. Different PET methods utilize different manually designed modules. In a small PLM, there are usually noticeable performance differences among PET methods. Nevertheless, when a PLM's scale grows up to tens of billions of parameters, all PET methods achieve almost the same performance and even perform on par with the full-parameter fine-tuning method. Hence, we hypothesize that model scaling can mitigate the design differences (the module structures and the number of trainable parameters) among PET methods. To study this hypothesis, we introduce a more flexible PET method - arbitrary PET (APET) method - to be compatible with arbitrary module structures and any number of trainable parameters. Then, we experiment on $11$ NLP tasks of $5$ types and $2$ representative PLMs. From our investigations, we find that the model scaling (1) mitigates the effects of the arbitrary module structure on the performance of tuning methods, and (2) enables the tuning methods to optimize fewer parameters to achieve the full-parameter fine-tuning performance. Intriguingly, we also observe that all tuning methods require almost the same number of trainable parameters to drive PLMs. We discuss this phenomenon and the above two findings collectively from optimization perspectives to fathom the mechanisms behind them. These conclusions not only demonstrate the positive impact of model scaling on tuning methods but disclose its mechanisms, which help us design more effective and efficient tuning methods on larger-scale PLMs

Cardiovascular outcomes and safety of SGLT2 inhibitors in chronic kidney disease patients

BackgroundSodium–glucose co-transporter 2 (SGLT2) inhibitors provide cardiovascular protection for patients with heart failure (HF) and type 2 diabetes mellitus (T2DM). However, there is little evidence of their application in patients with chronic kidney disease (CKD). Furthermore, there are inconsistent results from studies on their uses. Therefore, to explore the cardiovascular protective effect of SGLT2 inhibitors in the CKD patient population, we conducted a systematic review and meta-analysis to evaluate the cardiovascular effectiveness and safety of SGLT2 inhibitors in this patient population.MethodWe searched the PubMed® (National Library of Medicine, Bethesda, MD, USA) and Web of Science™ (Clarivate™, Philadelphia, PA, USA) databases for randomized controlled trials (RCTs) of SGLT2 inhibitors in CKD patients and built the database starting in January 2023. In accordance with our inclusion and exclusion criteria, the literature was screened, the quality of the literature was evaluated, and the data were extracted. RevMan 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark) and Stata® 17.0 (StataCorp LP, College Station, TX, USA) were used for the statistical analyses. Hazard ratios (HRs), odds ratios (ORs), and corresponding 95% confidence intervals (CIs) were used for the analysis of the outcome indicators.ResultsThirteen RCTs were included. In CKD patients, SGLT2 inhibitors reduced the risk of cardiovascular death (CVD) or hospitalization for heart failure (HHF) by 28%, CVD by 16%. and HHF by 35%. They also reduced the risk of all-cause death by 14% without increasing the risk of serious adverse effects (SAEs) and urinary tract infections (UTIs). However, they increased the risk of reproductive tract infections (RTIs).ConclusionSGLT2 inhibitors have a cardiovascular protective effect on patients with CKD, which in turn can significantly reduce the risk of CVD, HHF, and all-cause death without increasing the risk of SAEs and UTIs but increasing the risk of RTIs

Gut microbiota: a newly identified environmental factor in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that predominantly affects women of childbearing age and is characterized by the damage to multiple target organs. The pathogenesis of SLE is complex, and its etiology mainly involves genetic and environmental factors. At present, there is still a lack of effective means to cure SLE. In recent years, growing evidence has shown that gut microbiota, as an environmental factor, triggers autoimmunity through potential mechanisms including translocation and molecular mimicry, leads to immune dysregulation, and contributes to the development of SLE. Dietary intervention, drug therapy, probiotics supplement, fecal microbiome transplantation and other ways to modulate gut microbiota appear to be a potential treatment for SLE. In this review, the dysbiosis of gut microbiota in SLE, potential mechanisms linking gut microbiota and SLE, and immune dysregulation associated with gut microbiota in SLE are summarized