DataSheet_1_Interannual variation of summer sea surface temperature in the Amundsen Sea, Antarctica.pdf

This paper investigates the interannual variability of January sea surface temperature (SST) in the Amundsen Sea (AS) during the period 1982–2022. SST in the Pine Island Bay (PIB) is found to exhibit the most significant interannual variation, with a standard deviation up to 0.6°. Composite analysis indicates that, in warmer years, the January SST at PIB is approximately 1° higher on average than that in cooler years, and its variation in warmer (cooler) years corresponds to lower (higher) sea ice concentration (SIC) and more (less) surface heat flux; the latter factor is primarily influenced by the albedo of SIC. Further analysis suggests that variability in January SIC is largely dominated by northward sea ice motion during the previous November (r = −0.82), which is consistent with the presence of a contemporaneous northerly 10 m wind anomaly trigged by the Amundsen Sea Low (ASL). The ASL-associated northerly wind anomaly drives northward sea ice motion, reduces SIC, and thus increases the downward heat flux that ultimately results in warmer SST, and vice versa. This study identifies the possible mechanism of anomalous January SST in the PIB, which could provide an important clue for seasonal forecasts of summer SST in the AS.</p

Image_1_Fast and flexible spatial sampling methods based on the Quadtree algorithm for ocean monitoring.tif

Although existing in situ oceanographic data are sparse, such data still play an important role in submarine monitoring and forecasting. Considering budget limitations, an efficient spatial sampling scheme is critical to obtain data with much information from as few sampling stations as possible. This study improved existing sampling methods based on the Quadtree (QT) algorithm. In the first-phase sampling, the gradient-based QT (GQT) algorithm is recommended since it avoids the repeated calculation of variance in the Variance QT (VQT) algorithm. In addition, based on the GQT algorithm, we also propose the algorithm considering the change in variation (the GGQT algorithm) to alleviate excessive attention to the area with large changes. In second-phase sampling, QT decomposition and the greedy algorithm are combined (the BG algorithm). QT decomposition is used to divide the region into small blocks first, and then within the small blocks, the greedy algorithm is applied to sampling simultaneously. In terms of sampling efficiency, both the GQT (GGQT) algorithm and the BG algorithm are close to the constant time complexity, which is much lower than the time consumption of the VQT algorithm and the dynamic greedy (DG) algorithm and conducive to large-scale sampling tasks. At the same time, the algorithms recommend above share similar qualities with the VQT algorithm and the dynamic greedy algorithm.</p

DataSheet_1_Fast and flexible spatial sampling methods based on the Quadtree algorithm for ocean monitoring.docx

Although existing in situ oceanographic data are sparse, such data still play an important role in submarine monitoring and forecasting. Considering budget limitations, an efficient spatial sampling scheme is critical to obtain data with much information from as few sampling stations as possible. This study improved existing sampling methods based on the Quadtree (QT) algorithm. In the first-phase sampling, the gradient-based QT (GQT) algorithm is recommended since it avoids the repeated calculation of variance in the Variance QT (VQT) algorithm. In addition, based on the GQT algorithm, we also propose the algorithm considering the change in variation (the GGQT algorithm) to alleviate excessive attention to the area with large changes. In second-phase sampling, QT decomposition and the greedy algorithm are combined (the BG algorithm). QT decomposition is used to divide the region into small blocks first, and then within the small blocks, the greedy algorithm is applied to sampling simultaneously. In terms of sampling efficiency, both the GQT (GGQT) algorithm and the BG algorithm are close to the constant time complexity, which is much lower than the time consumption of the VQT algorithm and the dynamic greedy (DG) algorithm and conducive to large-scale sampling tasks. At the same time, the algorithms recommend above share similar qualities with the VQT algorithm and the dynamic greedy algorithm.</p

Integrative transcriptome analysis identifies genes and pathways associated with enzalutamide resistance of prostate cancer

<p><b>Background:</b> Enzalutamide, a novel androgen receptor (AR) signaling inhibitor, has been widely used to increase survival in patients with castration-resistant prostate cancer. However, resistance to enzalutamide invariably develops.</p> <p><b>Methods:</b> To understand the underlying mechanisms of resistance to enzalutamide, we performed integrative analysis on multiple transcriptome datasets to identify those genes constantly up- or down-regulated in response to enzalutamide treatment.</p> <p><b>Results:</b> There were 703 and 581 differentially expressed genes derived from enzalutamide-sensitive and -resistant cell lines, respectively. Functional enrichment analysis on these genes demonstrated that biological processes of cell proliferation and ubiquitin mediated proteolysis pathway are specifically disturbed in sensitive cell lines but not resistant ones. Such divergence explained why enzalutamide ineffective for resistant prostate cancer.</p> <p><b>Conclusions:</b> Taken together, the present study revealed a set of critical genes, which can provide etiologic clues as to enzalutamide-resistant prostate cancer and guide novel therapeutic approaches.</p

The most enriched GO terms (level 2) in unigenes of <i>S</i>. <i>miltiorrhiza</i> cell cultures.

<p>All 17 867 unigenes predominantly belonged to ‘Catalytic activity’ and ‘Binding’ under Molecular function, ‘Cell part’ and ‘Cell’ under Cellular component, and ‘Metabolic process’ and ‘Cellular process’ under Biological process. The number of unigenes belonging to each category are provided.</p

Effect of SA on antioxidative enzymes and GSH in <i>S</i>. <i>miltiorrhiza</i> cell cultures.

<p>(a) Effect of SA on isozymograms of SOD (left) and POD (right). 1 represents control and 2 represents SA treatment for 2 h. a, b, c, d represents four bands of SOD and POD, respectively. (b) Effect of SA on enzyme activities of SOD and POD. (c) Effect of SA on the content of GSH. Significance was indicated by double or single asterisks with <i>p</i>-values below 0.01 or between 0.01 and 0.05, respectively.</p

Functional analysis of DEGs in <i>S</i>. <i>miltiorrhiza</i> cell cultures after SA induction.

<p>(a) Hierarchically clustered heat map for the expression profile of DEGs (reflected as log₂ FC when compared to control), which consist of 1584 up-regulated (left), 1492 down-regulated (middle) and 2240 inconsistently regulated DEGs (right) after 8h SA induction. Blue represent repression, whereas red represent induction. (b) Analysis of biological process category of DEGs including up-regulated (red) and down-regulated (green) in <i>S</i>. <i>miltiorrhiza</i> cells after 8h SA induction. Enrichment was measured by comparing the number of DEGs from each category with the total number of genes for that GO term and using Fisher’s exact test. Significance indicated <i>p</i>-values below 0.01 or between 0.01 and 0.05, respectively.</p

mRNA profiling of SA induced <i>S</i>. <i>miltiorrhiza</i> cell cultures by RNA-seq.

<p>(a) The common and unique expression profiles among sample groups. Numbers represent expressed unigenes in control (0 h) and SA (2 h and 8 h) treated cell cultures. (b) Number of DEGs found among different sample groups, according to a FDR< 0.01 and FC ≥ 2 or ≤ -2. T1, T2 belong to control group, T3, T4 and T5 belong to treatment group of SA induction for 2 h, T6, T7 and T8 belong to treatment group of SA induction for 8 h. (c) Length distribution of the 50 778 assembled unigenes (digital details see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0147849#pone.0147849.s007" target="_blank">S3 Table</a>).</p

KEGG classifications of the DEGs in <i>S</i>. <i>miltiorrhiza</i> cell cultures under SA induction.

<p>A total of 532 DEGs were assigned to 104 KEGG pathways. The DEGs predominantly belonged to ‘Plant hormone signal transduction’ and ‘Plant-pathogen interaction’. The number of DEGs belonging to each category are provided.</p

Cadmium Isotope Fractionations Induced by Foliar and Root Uptake for Rice Exposed to Atmospheric Particles: Implications for Environmental Source Tracing

Rice roots and leaves were exposed to cadmium (Cd)-contaminated particles in a greenhouse to modify the fractionation during Cd uptake and transport in the plants by using the Bayesian mixing model. The exposure to atmospheric Cd in rice near a copper smelter was determined under the field conditions. The results showed that the leaves directly absorb and accumulate atmospheric Cd in other aboveground tissues by 84–99%. Positive values of Δ114/110CdLeaf–Particle (0.08–0.11‰), Δ114/110CdNode–Leaf (0.77–0.81‰), and Δ114/110CdGrain–Leaf (0.39–0.43‰) following foliar exposure as well as Δ114/110CdRoot–Particle (0.19–0.22‰) and Δ114/110CdGrain–Root (−0.29 to 0.34‰) following root exposure suggested that particles released heavy isotopes. The roots and leaves preferentially retained light isotopes and transported heavy isotopes upward. The Cd isotope fractionations in the plant were constant under both root and foliar exposures at two dosages. The results were used to correct the parameters in the isotope mixing models and to trace the source of atmospheric Cd in rice tissues in the field. Both models of isotope mixing and mass balance suggested 50–63% Cd deposition on the grains. This study provides a theoretical basis for tracing the source of Cd isotopes in plants