Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term

Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorporating sharpness as a regularization term. We prove its generalization bound through the combination of PAC and Bayes-PAC techniques, and evaluate its performance on various public datasets. The results demonstrate that WSAM achieves improved generalization, or is at least highly competitive, compared to the vanilla optimizer, SAM and its variants. The code is available at https://github.com/intelligent-machine-learning/dlrover/tree/master/atorch/atorch/optimizers.Comment: 10 pages. Accepted as a conference paper at KDD '2

Diurnal and Seasonal Patterns of Methane Emissions from a Dairy Operation in North China Plain

In China, dairy cattle managed in collective feedlots contribute about 30% of the milk production and are believed to be an important contributor to national methane emissions. Methane emissions from a collective dairy feedlot in North China Plain (NCP) were measured during the winter, spring, summer, and fall seasons with open-path lasers in combination with an inverse dispersion technique. Methane emissions from the selected dairy feedlot were characterized by an apparent diurnal pattern with three peaks corresponding to the schedule of feeding activities. On a per capita basis, daily methane emission rates of these four seasons were 0.28, 0.32, 0.33, and 0.30 kg head−1 d−1, respectively. In summary, annual methane emission rate was 112.4 kg head−1 yr−1 associated with methane emission intensity of 32.65 L CH4 L−1 of milk and potential methane conversion factor Ym of 6.66% of gross energy intake for mature dairy cows in North China Plain

Identifying Obstructive Hypertrophic Cardiomyopathy from Nonobstructive Hypertrophic Cardiomyopathy: Development and Validation of a Model Based on Electrocardiogram Features

Background: The clinical presentation and prognosis of hypertrophic cardiomyopathy (HCM) are heterogeneous between nonobstructive HCM (HNCM) and obstructive HCM (HOCM). Electrocardiography (ECG) has been used as a screening tool for HCM. However, it is still unclear whether the features presented on ECG could be used for the initial classification of HOCM and HNCM. Objective: We aimed to develop a pragmatic model based on common 12-lead ECG features for the initial identification of HOCM/HNCM. Methods: Between April 1st and September 30th, 2020, 172 consecutive HCM patients from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital were prospectively included in the training cohort. Between January 4th and February 30th, 2021, an additional 62 HCM patients were prospectively included in the temporal internal validation cohort. External validation was performed using retrospectively collected ECG data with definite classification (390 HOCM and 499 HNCM ECG samples) from January 1st, 2010 to March 31st, 2020. Multivariable backward logistic regression (LR) was used to develop the prediction model. The discrimination performance, calibration and clinical utility of the model were evaluated. Results: Of all 30 acquired ECG parameters, 10 variables were significantly different between HOCM and HNCM (all P < 0.05). The P wave interval and SV1 were selected to construct the model, which had a clearly useful C-statistic of 0.805 (0.697, 0.914) in the temporal validation cohort and 0.776 (0.746, 0.806) in the external validation cohort for differentiating HOCM from HNCM. The calibration plot, decision curve analysis, and clinical impact curve indicated that the model had good fitness and clinical utility. Conclusion: The pragmatic model constructed by the P wave interval and SV1 had a clearly useful ability to discriminate HOCM from HNCM. The model might potentially serve as an initial classification of HCM before referring patients to dedicated centers and specialists. Highlights What are the novel findings of this work? • Evident differences exist in the ECG presentations between HOCM and HNCM. • To the best of our knowledge, this study is the first piece of evidence to quantify the difference in the ECG presentations between HOCM and HNCM. • Based on routine 12-lead ECG data, a probabilistic model was generated that might assist in the initial classification of HCM patients

Benchmark analysis for robustness of multi-scale urban road networks under global disruptions

To date immunity to disruptions of multi-scale urban road networks (URNs) has not been effectively quantified. This study uses robustness as a meaningful - if partial - representation of immunity. We propose a novel Relative Area Index (RAI) based on traffic assignment theory to quantitatively measure the robustness of URNs under global capacity degradation due to three different types of disruptions, which takes into account many realistic characteristics. We also compare the RAI with weighted betweenness centrality, a traditional topological metric of robustness. We employ six realistic URNs as case studies for this comparison. Our analysis shows that RAI is a more effective measure of the robustness of URNs when multi-scale URNs suffer from global disruptions. This improved effectiveness is achieved because of RAI's ability to capture the effects of realistic network characteristics such as network topology, flow patterns, link capacity, and travel demand. Also, the results highlight the importance of central management when URNs suffer from disruptions. Our novel method may provide a benchmark tool for comparing robustness of multi-scale URNs, which facilitates the understanding and improvement of network robustness for the planning and management of URNs

Dynamic Transformation of Nano-MoS2 in a Soil-Plant System Empowers Its Multifunctionality on Soybean Growth

Molybdenum disulfide (nano-MoS2) nanomaterials have shown great potential for biomedical and catalytic applications due to their unique enzyme-mimicking properties. However, their potential agricultural applications have been largely unexplored. A key factor prior to the application of nano-MoS2 in agriculture is understanding its behavior in a complex soil-plant system, particularly in terms of its transformation. Here, we investigate the distribution and transformation of two types of nano-MoS2 (MoS2 nanoparticles and MoS2 nanosheets) in a soil-soybean system through a combination of synchrotron radiation-based X-ray absorption near-edge spectroscopy (XANES) and single-particle inductively coupled plasma mass spectrometry (SP-ICP-MS). We found that MoS2 nanoparticles (NPs) transform dynamically in soil and plant tissues, releasing molybdenum (Mo) and sulfur (S) that can be incorporated gradually into the key enzymes involved in nitrogen metabolism and the antioxidant system, while the rest remain intact and act as nanozymes. Notably, there is 247.9 mg/kg of organic Mo in the nodule, while there is only 49.9 mg/kg of MoS2 NPs. This study demonstrates that it is the transformation that leads to the multifunctionality of MoS2, which can improve the biological nitrogen fixation (BNF) and growth. Therefore, MoS2 NPs enable a 30% increase in yield compared to the traditional molybdenum fertilizer (Na2MoO4). Excessive transformation of MoS2 nanosheets (NS) leads to the overaccumulation of Mo and sulfate in the plant, which damages the nodule function and yield. The study highlights the importance of understanding the transformation of nanomaterials for agricultural applications in future studies.</p

Genome-wide characterization of SOS1 gene family in potato (Solanum tuberosum) and expression analyses under salt and hormone stress

Salt Overly Sensitive 1 (SOS1) is one of the members of the Salt Overly Sensitive (SOS) signaling pathway and plays critical salt tolerance determinant in plants, while the characterization of the SOS1 family in potato (Solanum tuberosum) is lacking. In this study, 37 StSOS1s were identified and found to be unevenly distributed across 10 chromosomes, with most of them located on the plasma membrane. Promoter analysis revealed that the majority of these StSOS1 genes contain abundant cis-elements involved in various abiotic stress responses. Tissue specific expression showed that 21 of the 37 StSOS1s were widely expressed in various tissues or organs of the potato. Molecular interaction network analysis suggests that 25 StSOS1s may interact with other proteins involved in potassium ion transmembrane transport, response to salt stress, and cellular processes. In addition, collinearity analysis showed that 17, 8, 1 and 5 of orthologous StSOS1 genes were paired with those in tomato, pepper, tobacco, and Arabidopsis, respectively. Furthermore, RT-qPCR results revealed that the expression of StSOS1s were significant modulated by various abiotic stresses, in particular salt and abscisic acid stress. Furthermore, subcellular localization in Nicotiana benthamiana suggested that StSOS1-13 was located on the plasma membrane. These results extend the comprehensive overview of the StSOS1 gene family and set the stage for further analysis of the function of genes in SOS and hormone signaling pathways

Anti-osteoporosis mechanism of resistance exercise in ovariectomized rats based on transcriptome analysis: a pilot study

Postmenopausal osteoporosis is the main cause of fractures in women. Resistance exercise has a positive effect on bone mineral density in postmenopausal osteoporosis patients, but its mechanism is unclear. The purpose of this study was to explore the mechanism of resistance exercise in improving ovariectomized osteoporotic rats based on the transcriptome sequencing technique. Eighteen female Sprague-Dawley rats were randomly divided into the sham-operated group, the non-exercise group, and the resistance exercise group. The rat model of postmenopausal osteoporosis was established by bilateral ovariectomy. Ten weeks after the operation, the resistance exercise group received 2 weeks of adaptive training, and 12 weeks of resistance exercise began in the 13th week. The rats were trained 5 days per week, in 4 sets of 3 repetitions per day. After the intervention, all rats were sacrificed, and the body weight, bone mineral density, trabecular bone microarchitecture, and bone biomechanics were examined. At the same time, RNA-seq and enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes were performed on the left tibias, followed by Elisa and RT-qPCR verification. It had been found that resistance exercise can effectively counteract the weight gain of ovariectomized osteoporotic rats, and has a good effect on bone mineral density and trabecular bone microarchitecture. Enrichment analysis showed that regulation of gene expression and osteoclast differentiation is the most closely related biological process and signaling pathway shared by RE/Ovx and NE/Ovx groups. Our results revealed that resistance exercise can play a role in inhibiting osteoclast activation and preventing the enhancement of osteoclast bone resorption function in ovariectomized osteoporotic rats by inhibiting Fos/Fosb-regulated TRAP activation and relieving Calcr inhibition, which has important application value in preventing bone loss caused by estrogen deficiency