Examples of the most commonly used functions in SRplot.

(A) Cluster heatmap; (B) motif logo; (C) Enrichment GO term (BP/CC/MF); (D) Two tracks circus histogram; (E) SNP density; (F) Enhanced volcano plot; (G) KM survival curve; (H) Circle correlation pearson; (I) Correlation plot.</p

Outline of functions in SRplot.

Graphics are widely used to provide summarization of complex data in scientific publications. Although there are many tools available for drawing graphics, their use is limited by programming skills, costs, and platform specificities. Here, we presented a freely accessible easy-to-use web server named SRplot that integrated more than a hundred of commonly used data visualization and graphing functions together. It can be run easily using all Web browsers and there are no strong requirements on the computing power of users’ machines. With a user-friendly graphical interface, users can simply paste the contents of the input file into the text box according to the defined file format. Modification operations can be easily performed, and graphs can be generated in real-time. The resulting graphs can be easily downloaded in bitmap (PNG or TIFF) or vector (PDF or SVG) format in publication quality. The website is updated promptly and continuously. Functions in SRplot have been improved, optimized and updated depend on feedback and suggestions from users. The graphs prepared with SRplot have been featured in more than five hundred peer-reviewed publications. The SRplot web server is now freely available at http://www.bioinformatics.com.cn/SRplot.</div

Comparison among platforms and software for data visualization and graphing.

Comparison among platforms and software for data visualization and graphing.</p

DataSheet_3_Study on the seasonal variations of dimethyl sulfide, its precursors and their impact factors in the Bohai Sea and North Yellow Sea.zip

Dimethyl sulfide (DMS) is one of the most important volatile biogenic sulfur compounds and plays a significant role in global climate change. Studying the seasonal variations and the environmental factors that affect the concentration of DMS would aid in understanding the biogeochemical cycle of sulfur compounds. Using benzene-assisted photoionization positive ion mobility spectrometry (BAPI-PIMS), the seasonal distribution and the key impact factors of DMS and dimethylsulfoniopropionate (DMSP) in the Bohai Sea and North Yellow Sea were investigated in the summer and autumn of 2019. The concentrations of DMS and its precursors, DMSPp and DMSPd, in the surface seawater were 0.11–23.90, 0.67–41.38, and 0.03–12.28 nmol/L, respectively, in summer, and 0.10–20.79, 0.39–13.51, and 0.18–20.58 nmol/L, respectively, in autumn. The air-to-sea exchange flux of DMS was 43.05 ± 44.52 and 34.06 ± 63.38 μmol/(m·d), respectively, in summer and autumn. The results demonstrated that the temperature was the most dominant environmental factor, and the abundance of dinoflagellates was the most dominant biological factor that affected the distribution of DMS and DMSP in summer. The abundance of diatoms was the most dominant biological factor, and the levels of PO43-, NO2-, NO3-, and SiO32- were the dominant environmental factors that affected the distribution of DMS and DMSP in autumn. These results of this study would be of great significance in understanding the biochemical cycle of DMS in BS and NYS.</p

DataSheet_4_Study on the seasonal variations of dimethyl sulfide, its precursors and their impact factors in the Bohai Sea and North Yellow Sea.zip

Dimethyl sulfide (DMS) is one of the most important volatile biogenic sulfur compounds and plays a significant role in global climate change. Studying the seasonal variations and the environmental factors that affect the concentration of DMS would aid in understanding the biogeochemical cycle of sulfur compounds. Using benzene-assisted photoionization positive ion mobility spectrometry (BAPI-PIMS), the seasonal distribution and the key impact factors of DMS and dimethylsulfoniopropionate (DMSP) in the Bohai Sea and North Yellow Sea were investigated in the summer and autumn of 2019. The concentrations of DMS and its precursors, DMSPp and DMSPd, in the surface seawater were 0.11–23.90, 0.67–41.38, and 0.03–12.28 nmol/L, respectively, in summer, and 0.10–20.79, 0.39–13.51, and 0.18–20.58 nmol/L, respectively, in autumn. The air-to-sea exchange flux of DMS was 43.05 ± 44.52 and 34.06 ± 63.38 μmol/(m·d), respectively, in summer and autumn. The results demonstrated that the temperature was the most dominant environmental factor, and the abundance of dinoflagellates was the most dominant biological factor that affected the distribution of DMS and DMSP in summer. The abundance of diatoms was the most dominant biological factor, and the levels of PO43-, NO2-, NO3-, and SiO32- were the dominant environmental factors that affected the distribution of DMS and DMSP in autumn. These results of this study would be of great significance in understanding the biochemical cycle of DMS in BS and NYS.</p

DataSheet_2_Study on the seasonal variations of dimethyl sulfide, its precursors and their impact factors in the Bohai Sea and North Yellow Sea.zip

Dimethyl sulfide (DMS) is one of the most important volatile biogenic sulfur compounds and plays a significant role in global climate change. Studying the seasonal variations and the environmental factors that affect the concentration of DMS would aid in understanding the biogeochemical cycle of sulfur compounds. Using benzene-assisted photoionization positive ion mobility spectrometry (BAPI-PIMS), the seasonal distribution and the key impact factors of DMS and dimethylsulfoniopropionate (DMSP) in the Bohai Sea and North Yellow Sea were investigated in the summer and autumn of 2019. The concentrations of DMS and its precursors, DMSPp and DMSPd, in the surface seawater were 0.11–23.90, 0.67–41.38, and 0.03–12.28 nmol/L, respectively, in summer, and 0.10–20.79, 0.39–13.51, and 0.18–20.58 nmol/L, respectively, in autumn. The air-to-sea exchange flux of DMS was 43.05 ± 44.52 and 34.06 ± 63.38 μmol/(m·d), respectively, in summer and autumn. The results demonstrated that the temperature was the most dominant environmental factor, and the abundance of dinoflagellates was the most dominant biological factor that affected the distribution of DMS and DMSP in summer. The abundance of diatoms was the most dominant biological factor, and the levels of PO43-, NO2-, NO3-, and SiO32- were the dominant environmental factors that affected the distribution of DMS and DMSP in autumn. These results of this study would be of great significance in understanding the biochemical cycle of DMS in BS and NYS.</p

Data_Sheet_1_Diversity, structure, and distribution of bacterioplankton and diazotroph communities in the Bay of Bengal during the winter monsoon.docx

The Bay of Bengal (BoB) is conventionally believed to be a low productive, oligotrophic marine ecosystem, where the diazotroph communities presumed to play a vital role in adding “new” nitrogen through the nitrogen fixation process. However, the diazotroph communities in the oceanic region of the BoB are still poorly understood though it represents most of the seawater volume. The present study investigated a detailed account of the bacterioplankton community structure and distribution in the oceanic BoB during the winter monsoon using high throughput sequencing targeting the 16S rRNA and nifH genes. Our study observed diverse groups of bacterioplankton communities in the BoB including both cyanobacterial and non-cyanobacterial phylotypes. Cyanobacteria (Prochlorococcus spp. and Synechococcus spp.) and Proteobacteria (mainly α-, γ-, and δ-Proteobacteria) were the most abundant groups within the bacterial communities, possessing differential vertical distribution patterns. Cyanobacteria were more abundant in the surface waters, whereas Proteobacteria dominated the deeper layers (75 m). However, within the diazotroph communities, Proteobacteria (mainly γ-Proteobacteria) were the most dominant groups than Cyanobacteria. Function prediction based on PICRUSt revealed that nitrogen fixation might more active to add fixed nitrogen in the surface waters, while nitrogen removal pathways (denitrification and anammox) might stronger in deeper layers. Canonical correspondence analysis (CCA) indicated that temperature, salinity, and silicate were major environmental factors driving the distribution of bacterial communities. Additionally, phosphate was also an important factor in regulating the diazotroph communities in the surface water. Overall, this study provided detailed information on bacterial communities and their vital role in the nitrogen cycles in oligotrophic ecosystems.</p

DataSheet_1_Study on the seasonal variations of dimethyl sulfide, its precursors and their impact factors in the Bohai Sea and North Yellow Sea.zip

Dimethyl sulfide (DMS) is one of the most important volatile biogenic sulfur compounds and plays a significant role in global climate change. Studying the seasonal variations and the environmental factors that affect the concentration of DMS would aid in understanding the biogeochemical cycle of sulfur compounds. Using benzene-assisted photoionization positive ion mobility spectrometry (BAPI-PIMS), the seasonal distribution and the key impact factors of DMS and dimethylsulfoniopropionate (DMSP) in the Bohai Sea and North Yellow Sea were investigated in the summer and autumn of 2019. The concentrations of DMS and its precursors, DMSPp and DMSPd, in the surface seawater were 0.11–23.90, 0.67–41.38, and 0.03–12.28 nmol/L, respectively, in summer, and 0.10–20.79, 0.39–13.51, and 0.18–20.58 nmol/L, respectively, in autumn. The air-to-sea exchange flux of DMS was 43.05 ± 44.52 and 34.06 ± 63.38 μmol/(m·d), respectively, in summer and autumn. The results demonstrated that the temperature was the most dominant environmental factor, and the abundance of dinoflagellates was the most dominant biological factor that affected the distribution of DMS and DMSP in summer. The abundance of diatoms was the most dominant biological factor, and the levels of PO43-, NO2-, NO3-, and SiO32- were the dominant environmental factors that affected the distribution of DMS and DMSP in autumn. These results of this study would be of great significance in understanding the biochemical cycle of DMS in BS and NYS.</p

Bacterial growth-inhibition activities of breast milk samples.

<p>Colony-forming units of (A) <i>S. epidermidis</i>; (B) <i>S. aureus</i>; (C) <i>E. coli</i>; or (D) <i>S. agalactiae</i>, after 4 h incubation in either LBWF (F; n = 16), day 7 (n = 40) or day 21 (n = 31) skimmed preterm breast milk samples from participants in the case-control study. The dashed line shows median starting inoculum. Data show individual and median values on a log scale. A value of 10³ CFU/mL was assigned to samples where the colony count was below the limit of detection of the assay. Symbols depict the groups where statistically significant comparisons were made (level of significance indicated by multiple symbols; e.g. *p <0.05, **p<0.01, ***p<0.001), comparing growth in LBWF to growth in preterm breast milk samples by ANOVA with Dunn’s multiple comparison test (*) or comparing growth in day 7 and day 21 paired breast milk samples by Wilcoxon signed-rank tests (†).</p

Comparison of clinical data for cases and controls used in the nested case-control study of breast milk antimicrobial molecules.

<p>Infant n = 20 in each group, however, at day 21, only 14 LOS infants and 17 non-LOS infants mothers’ provided milk. p<0.05 considered significant; shown in bold font.</p><p>^aMean±SD;</p><p>^bMedian (IQR);</p><p>^cProportion (%);</p><p>MOM = mothers’ own milk; PDHM = pasteurised donor human milk.</p><p>*Data were only collected on milk feeds to day 28, therefore infants who had not reached full doses by day 28 were designated as receiving milk at day 28 arbitrarily (n = 12 LOS, 5 non-LOS).</p><p>Comparison of clinical data for cases and controls used in the nested case-control study of breast milk antimicrobial molecules.</p