On Sub-ENSO Variability

Multichannel singular spectrum analysis (MSSA) of surface zonal wind, sea surface temperature (SST), 20° isotherm depth, and surface zonal current observations (between 1990 and 2004) identifies three coupled ocean–atmosphere modes of variability in the tropical Pacific: the El Niño–Southern Oscillation (ENSO), the annual cycle, and a mode with a 14–18-month period, which is referred to as sub-ENSO in this study. The sub-ENSO mode accounts for the near 18-month (near annual) variability prior to (following) the 1997/98 El Niño event. It was strongest during this El Niño event, with SST anomalies exceeding 1°C. Sub-ENSO peak SST anomalies are ENSO-like in structure and are associated with eastward propagating heat content variations. However, the SST anomalies are preceded by and in near quadrature with relatively strong remotely forced westward propagating zonal current variations, suggesting the sub-ENSO mode arises from the zonal-advective feedback. The sub-ENSO mode is found to exist also in an intermediate complexity model (ICM) of the tropical Pacific. A heat budget analysis of the model’s sub-ENSO mode shows it indeed arises from the zonal-advective feedback. In the model, both ENSO and sub-ENSO modes coexist, but there is a weak nonlinear interaction between them. Experiments also show that the observed changes in sub-ENSO’s characteristics may be explained by changes in the relative importance of zonal and vertical advection SST tendencies

Derivation of the green vegetation fraction from TM data of three gorges area

AbstractFraction of green vegetation, fg is needed as one of regular parameters for vegetation cover analysis. The paper explores the potentials of deriving this variable from thermal mapper (TM) normalized difference vegetation index (NDVI) data considering the leaf area index (LAI) of agricultural field. Geometric, radiometric and atmospheric correction of the images were performed before further analysis. According to the sub-pixel structure characteristic, we choose mosaic-pixel model for calculating percent vegetation cover. A new method was put forward to achieve LAI values of non-dense vegetation where soil line equation was considered. Two schemes are produced to obtain different LAI values and type-specific accuracy is evaluated using parameter defined in this paper

Sulforaphane induces adipocyte browning and promotes glucose and lipid utilization

Scope: Obesity is closely related to the imbalance of white adipose tissue storing excess calories, and brown adipose tissue dissipating energy to produce heat in mammals. Recent studies revealed that acquisition of brown characteristics by white adipocytes, termed “browning,” may positively contribute to cellular bioenergetics and metabolism homeostasis. The goal was to investigate the putative effects of natural antioxidant sulforaphane (1-isothiocyanate-4-methyl-sulfonyl butane; SFN) on browning of white adipocytes. Methods and Results: 3T3-L1 mature white adipocytes were treated with SFN for 48 h, and then the mitochondrial content, function, and energy utilization were assessed. SFN was found to induce 3T3-L1 adipocytes browning based on the increased mitochondrial content and activity of respiratory chain enzymes, whereas the mechanism involved the upregulation of nuclear factor E2-related factor 2/ sirtuin1/ peroxisome proliferator-activated receptor gamma coactivator 1 alpha signaling. SFN enhanced uncoupling protein 1 expression, a marker for brown adipocyte, leading to the decrease in cellular ATP. SFN also enhanced glucose uptake and oxidative utilization, lipolysis and fatty acid oxidation in 3T3-L1 adipocytes. Conclusion: SFN-induced browning of white adipocytes enhanced the utilization of cellular fuel, and the application of SFN is a promising strategy to combat obesity and obesity-related metabolic disorder

Bis(6-meth­oxy-2-{[tris­(hydroxy­meth­yl)methyl-κO]imino­meth­yl}phenolato-κ2 N,O 1)nickel(II) dihydrate

In the title compound, [Ni(C12H16NO5)2]·2H2O, the NiII atom is coordinated by four O atoms and two N atoms from the two 6-meth­oxy-2-{[tris­(hydroxy­meth­yl)meth­yl]imino­meth­yl}phenolate ligands in a distorted octa­hedral coordination geometry. O—H⋯O hydrogen bonds link the complexes and uncoordinated water mol­ecules into two-dimensional networks parallel to (001)

The level of IL-35 in the circulation of patients with Graves’ disease

Introduction: The aim of this study was to explore the potential role of IL-35 in Graves’ disease (GD). Material and methods: A total of 142 GD patients including 80 newly onset patients, 52 refractory patients and 10 remission patients and 70 normal controls (NCs) were recruited. The messenger RNA (mRNA) expressions of P35 and Epstein-Barr-virus-induced gene 3 (Ebi3) were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Serum level of IL-35 was measured by enzyme-linked immunosorbent assay (ELISA). Results: The expression of IL-35mRNA in new onset GD and refractory GD were both significantly higher than NC. Comparison between remission GD and NC showed no significant difference (p > 0.05). A significant increase of Ebi3mRNA expression was observed in new onset GD compared with remission GD (p = 0.030). The new onset GD showed a tendency for increased expression of serum IL-35 but without significant difference. No correlation between IL-35 expression and clinic parameters was found. Conclusions: Our preliminary observations indicate that IL-35 and CD4+P35+Ebi3+T cells may be involved in the pathogenesis of GD

Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning

In contrastive self-supervised learning, the common way to learn discriminative representation is to pull different augmented "views" of the same image closer while pushing all other images further apart, which has been proven to be effective. However, it is unavoidable to construct undesirable views containing different semantic concepts during the augmentation procedure. It would damage the semantic consistency of representation to pull these augmentations closer in the feature space indiscriminately. In this study, we introduce feature-level augmentation and propose a novel semantics-consistent feature search (SCFS) method to mitigate this negative effect. The main idea of SCFS is to adaptively search semantics-consistent features to enhance the contrast between semantics-consistent regions in different augmentations. Thus, the trained model can learn to focus on meaningful object regions, improving the semantic representation ability. Extensive experiments conducted on different datasets and tasks demonstrate that SCFS effectively improves the performance of self-supervised learning and achieves state-of-the-art performance on different downstream tasks