Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks

Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration.Lake Dianchi is a shallow and turbid lake, located in Southwest China. Since 1985, Lake Dianchi has experienced severe cyanabacterial blooms (dominated by Microcystis spp.). In extreme cases, the algal cell densities have exceeded three billion cells per liter. To predict and elucidate the population dynamics ofMicrocystis spp. in Lake Dianchi, a neural network based model was developed. The correlation coefficient (R 2) between the predicted algal concentrations by the model and the observed values was 0.911. Sensitivity analysis was performed to clarify the algal dynamics to the changes of environmental factors. The results of a sensitivity analysis of the neural network model suggested that small increases in pH could cause significantly reduced algal abundance. Further investigations on raw data showed that the response of Microcystis spp. concentration to pH increase was dependent on algal biomass and pH level. When Microcystis spp. population and pH were moderate or low, the response of Microcystis spp. population would be more likely to be positive in Lake Dianchi; contrarily, Microcystis spp. population in Lake Dianchi would be more likely to show negative response to pH increase when Microcystis spp. population and pH were high. The paper concluded that the extremely high concentration of algal population and high pH could explain the distinctive response of Microcystis spp. population to +1 SD (standard deviation) pH increase in Lake Dianchi. And the paper also elucidated the algal dynamics to changes of other environmental factors. One SD increase of water temperature (WT) had strongest positive relationship with Microcystis spp. biomass. Chemical oxygen demand (COD) and total phosphorus (TP) had strong positive effect on Microcystis spp. abundance while total nitrogen (TN), biological oxygen demand in five days (BOD5), and dissolved oxygen had only weak relationship with Microcystis spp. concentration. And transparency (Tr) had moderate positive relationship with Microcystis spp. concentration

Observation of a ppb mass threshoud enhancement in \psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p}) decay

The decay channel $\psi^\prime\to\pi^+\pi^-J/\psi(J/\psi\to\gamma p\bar{p})$ is studied using a sample of $1.06\times 10^8$ $\psi^\prime$ events collected by the BESIII experiment at BEPCII. A strong enhancement at threshold is observed in the $p\bar{p}$ invariant mass spectrum. The enhancement can be fit with an $S$-wave Breit-Wigner resonance function with a resulting peak mass of $M=1861^{+6}_{-13} {\rm (stat)}^{+7}_{-26} {\rm (syst)} {\rm MeV/}c^2$ and a narrow width that is $\Gamma<38 {\rm MeV/}c^2$ at the 90% confidence level. These results are consistent with published BESII results. These mass and width values do not match with those of any known meson resonance.Comment: 5 pages, 3 figures, submitted to Chinese Physics

A New Stalked Filter-Feeder from the Middle Cambrian Burgess Shale, British Columbia, Canada

Burgess Shale-type deposits provide invaluable insights into the early evolution of body plans and the ecological structure of Cambrian communities, but a number of species, continue to defy phylogenetic interpretations. Here we extend this list to include a new soft-bodied animal, Siphusauctum gregarium n. gen. and n. sp., from the Tulip Beds (Campsite Cliff Shale Member, Burgess Shale Formation) of Mount Stephen (Yoho National Park, British Columbia). With 1,133 specimens collected, S. gregarium is clearly the most abundant animal from this locality

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p

Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer

Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-κB, and Wnt/β-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms

Measurement of the matrix element for the decay η′→ηπ +π -

The Dalitz plot of η⊃′→ηπ⊃+π⊃- decay is studied using (225.2±2.8)×106 J/ψ events collected with the BESIII detector at the BEPCII e⊃+e⊃- collider. With the largest sample of η⊃′ decays to date, the parameters of the Dalitz plot are determined in a generalized and a linear representation. Also, the branching fraction of J/ψ→γη⊃′ is determined to be (4.84±0.03±0.24)×10⊃-3, where the first error is statistical and the second systematic. © 2011 American Physical Society.published_or_final_versio

First observation of the decays χcJ→π0π0π0π0

We present a study of the P-wave spin-triplet charmonium χ cJ decays (J=0, 1, 2) into π0π0π0π0. The analysis is based on 106×106 ψ⊃′ decays recorded with the BESIII detector at the BEPCII electron positron collider. The decay into the π0π0π0π0 hadronic final state is observed for the first time. We measure the branching fractions B(χ c0→π0π0π0π0)=(3.34±0. 06±0.44)×10⊃-3, B(χ c1→π0π0π0π0) =(0.57±0.03±0.08)×10⊃-3, and B(χ c2→π0π0π0π0)=(1.21±0.05±0.16) ×10⊃-3, where the uncertainties are statistical and systematical, respectively. © 2011 American Physical Society.published_or_final_versio

Higher-order multipole amplitude measurement in ψ ′→γχ c2

Using 106×106 ψ ′ events collected with the BESIII detector at the BEPCII storage ring, the higher-order multipole amplitudes in the radiative transition ψ ′→γχ c2→γπ +π -/γK +K - are measured. A fit to the χ c2 production and decay angular distributions yields M2=0.046±0. 010±0.013 and E3=0.015±0.008±0.018, where the first errors are statistical and the second systematic. Here M2 denotes the normalized magnetic quadrupole amplitude and E3 the normalized electric octupole amplitude. This measurement shows evidence for the existence of the M2 signal with 4.4σ statistical significance and is consistent with the charm quark having no anomalous magnetic moment. © 2011 American Physical Society.published_or_final_versio

Branching fraction measurements of χc0 and χc2 to π0π0 and ηη

Using a sample of 1.06×108 ψ ′ decays collected by the BESIII detector, χc0 and χc2 decays into π0π0 and ηη are studied. The branching fraction results are Br(χc0→π 0π0)=(3.23±0.03±0.23±0.14)×10 -3, Br(χc2→π0π0)=(8.8±0.2±0.6±0.4)×10 -4, Br(χc0→ηη)=(3.44±0.10±0. 24±0.2)×10 -3, and Br(χc2→ηη)=(6. 5±0.4±0.5±0.3)×10 -4, where the uncertainties are statistical, systematic due to this measurement, and systematic due to the branching fractions of ψ ′→ γχcJ. The results provide information on the decay mechanism of χc states into pseudoscalars. © 2010 The American Physical Society.published_or_final_versio