Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy

Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its surprising effectiveness in improving generalization performance.However, training neural networks with SAM can be highly unstable since the loss does not decrease along the direction of the exact gradient at the current point, but instead follows the direction of a surrogate gradient evaluated at another point nearby. To address this issue, we propose a simple renormalization strategy, dubbed StableSAM, so that the norm of the surrogate gradient maintains the same as that of the exact gradient. Our strategy is easy to implement and flexible enough to integrate with SAM and its variants, almost at no computational cost. With elementary tools from convex optimization and learning theory, we also conduct a theoretical analysis of sharpness-aware training, revealing that compared to stochastic gradient descent (SGD), the effectiveness of SAM is only assured in a limited regime of learning rate. In contrast, we show how StableSAM extends this regime of learning rate and when it can consistently perform better than SAM with minor modification. Finally, we demonstrate the improved performance of StableSAM on several representative data sets and tasks.Comment: 31 page

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

Background: The tricarboxylic acid cycle (TCA cycle) is an important metabolic pathway and closely related to tumor development. However, its role in the development of esophageal squamous cell carcinoma (ESCC) has not been fully investigated.Methods: The RNA expression profiles of ESCC samples were retrieved from the TCGA database, and the GSE53624 dataset was additionally downloaded from the GEO database as the validation cohort. Furthermore, the single cell sequencing dataset GSE160269 was downloaded. TCA cycle-related genes were obtained from the MSigDB database. A risk score model for ESCC based on the key genes of the TCA cycle was built, and its predictive performance was evaluated. The association of the model with immune infiltration and chemoresistance were analyzed using the TIMER database, the R package “oncoPredict” score, TIDE score and so on. Finally, the role of the key gene CTTN was validated through gene knockdown and functional assays.Results: A total of 38 clusters of 8 cell types were identified using the single-cell sequencing data. The cells were divided into two groups according to the TCA cycle score, and 617 genes were identified that were most likely to influence the TCA cycle. By intersecting 976 key genes of the TCA cycle with the results of WGCNA, 57 genes significantly associated with the TCA cycle were further identified, of which 8 were screened through Cox regression and Lasso regression to construct the risk score model. The risk score was a good predictor of prognosis across subgroups of age, N, M classification and TNM stage. Furthermore, BI-2536, camptothecin and NU7441 were identified as possible drug candidates in the high-risk group. The high-risk score was associated with decreased immune infiltration in ESCC, and the low-risk group had better immunogenicity. In addition, we also evaluated the relationship between risk scores and immunotherapy response rates. Functional assays showed that CTTN may affect the proliferation and invasion of ESCC cells through the EMT pathway.Conclusion: We constructed a predictive model for ESCC based on TCA cycle-associated genes, which achieved good prognostic stratification. The model are likely associated with the regulation of tumor immunity in ESCC

CD44: a cancer stem cell marker and therapeutic target in leukemia treatment

CD44 is a ubiquitous leukocyte adhesion molecule involved in cell-cell interaction, cell adhesion, migration, homing and differentiation. CD44 can mediate the interaction between leukemic stem cells and the surrounding extracellular matrix, thereby inducing a cascade of signaling pathways to regulate their various behaviors. In this review, we focus on the impact of CD44s/CD44v as biomarkers in leukemia development and discuss the current research and prospects for CD44-related interventions in clinical application

Storm surge variability and prediction from ENSO and tropical cyclones

Storm surges are among the deadliest natural hazards, but understanding and prediction of year-to-year variability of storm surges is challenging. Here, we demonstrate that the interannual variability of observed storm surge levels can be explained and further predicted, through a process-based study in Hong Kong. We find that ENSO exerts a compound impact on storm surge levels through modulating tropical cyclones (TCs) and other forcing factors. The occurrence frequencies of local and remote TCs are responsible for the remaining variability in storm surge levels after removing the ENSO effect. Finally, we show that a statistical prediction model formed by ENSO and TC indices has good skill for prediction of extreme storm surge levels. The analysis approach can be applied to other coastal regions where tropical storms and the climate variability are main contributors to storm surges. Our study gives new insight into identifying “windows of opportunity” for successful prediction of storm surges on long-range timescales

A novel liposomal S-propargyl-cysteine: a sustained release of hydrogen sulfide reducing myocardial fibrosis via TGF-β1/Smad pathway

Purpose: S-propargyl-cysteine (SPRC; alternatively known as ZYZ-802) is a novel modulator of endogenous tissue H2S concentrations with known cardioprotective and anti-inflammatory effects. However, its rapid metabolism and excretion have limited its clinical application. To overcome these issues, we have developed some novel liposomal carriers to deliver ZYZ-802 to cells and tissues and have characterized their physicochemical, morphological and pharmacological properties. Methods :Two liposomal formulations of ZYZ-802 were prepared by thin-layer hydration and the morphological characteristics of each liposome system were assessed using a laser particle size analyzer and transmission electron microscopy. The entrapment efficiency and ZYZ-802 release profiles were determined following ultrafiltration centrifugation, dialysis tube and HPLC measurements. LC-MS/MS was used to evaluate the pharmacokinetic parameters and tissue distribution profiles of each formulation via the measurements of plasma and tissues ZYZ-802 and H2S concentrations. Using an in vivo model of heart failure (HF), the cardio-protective effects of liposomal carrier were determined by echocardiography, histopathology, western blot and the assessment of antioxidant and myocardial fibrosis markers.Results: Both liposomal formulations improved ZYZ-802 pharmacokinetics and optimized H2S concentrations in plasma and tissues. Liposomal ZYZ-802 showed enhanced cardioprotective effects in vivo. Importantly, liposomal ZYZ-802 could inhibit myocardial fibrosis via the inhibition of the TGF-β1/Smad signaling pathway. Conclusion: The liposomal formulations of ZYZ-802 have enhanced pharmacokinetic and pharmacological properties in vivo. This work is the first report to describe the development of liposomal formulations to improve the sustained release of H2S within tissues.Key word: Liposome; S-Propargyl-cysteine (SPRC, ZYZ-802); Hydrogen sulfide; Heart failure; Myocardial fibrosis; TGF-β1/Smad pathwa

The Reading Palaeofire Database : an expanded global resource to document changes in fire regimes from sedimentary charcoal records

Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b - Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.Peer reviewe

The Reading Palaeofire Database: an expanded global resource to document changes in fire regimes from sedimentary charcoal records

Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345