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

Synthetic Aβ peptides acquire prion-like properties in the brain

In transmission studies with Alzheimer's disease (AD) animal models, the formation of Aβ plaques is proposed to be initiated by seeding the inoculated amyloid β (Aβ) peptides in the brain. Like the misfolded scrapie prion protein (PrP(Sc)) in prion diseases, Aβ in AD shows a certain degree of resistance to protease digestion while the biochemical basis for protease resistance of Aβ remains poorly understood. Using in vitro assays, histoblotting, and electron microscopy, we characterize the biochemical and morphological features of synthetic Aβ peptides and Aβ isolated from AD brain tissues. Consistent with previous observations, monomeric and oligomeric Aβ species extracted from AD brains are insoluble in detergent buffers and resistant to digestions with proteinase K (PK). Histoblotting of AD brain tissue sections exhibits an increased Aβ immunoreactivity after digestion with PK. In contrast, synthetic Aβ40 and Aβ42 are soluble in detergent buffers and fully digested by PK. Electron microscopy of Aβ40 and Aβ42 synthetic peptides shows that both species of Aβ form mature fibrils. Those generated from Aβ40 are longer but less numerous than those made of Aβ42. When spiked into human brain homogenates, both Aβ40 and Aβ42 acquire insolubility in detergent and resistance to PK. Our study favors the hypothesis that the human brain may contain cofactor(s) that confers the synthetic Aβ peptides PrP(Sc)-like physicochemical properties

EM23, a natural sesquiterpene lactone, targets thioredoxin reductase to activate JNK and cell death pathways in human cervical cancer cells

Sesquiterpene lactones (SLs) are the active constituents of a variety of medicinal plants and found to have potential anticancer activities. However, the intracellular molecular targets of SLs and the underlying molecular mechanisms have not been well elucidated. In this study, we observed that EM23, a natural SL, exhibited anti-cancer activity in human cervical cancer cell lines by inducing apoptosis as indicated by caspase 3 activation, XIAP downregulation and mitochondrial dysfunction. Mechanistic studies indicated that EM23-induced apoptosis was mediated by reactive oxygen species (ROS) and the knockdown of thioredoxin (Trx) or thioredoxin reductase (TrxR) resulted in a reduction in apoptosis. EM23 attenuated TrxR activity by alkylation of C-terminal redox-active site Sec498 of TrxR and inhibited the expression levels of Trx/TrxR to facilitate ROS accumulation. Furthermore, inhibition of Trx/TrxR system resulted in the dissociation of ASK1 from Trx and the downstream activation of JNK. Pretreatment with ASK1/JNK inhibitors partially rescued cells from EM23-induced apoptosis. Additionally, EM23 inhibited Akt/mTOR pathway and induced autophagy, which was observed to be proapoptotic and mediated by ROS. Together, these results reveal a potential molecular mechanism for the apoptotic induction observed with SL compound EM23, and emphasize its putative role as a therapeutic agent for human cervical cancer.published_or_final_versio

Wall-thickness-dependent strength of nanotubular ZnO

We fabricate nanotubular ZnO with wall thickness of 45, 92, 123 nm using nanoporous gold (np-Au) with ligament diameter at necks of 1.43 mu m as sacrificial template. Through micro-tensile and micro-compressive testing of nanotubular ZnO structures, we find that the exponent m in (sigma) over bar proportional to (rho) over bar (m), where (sigma) over bar is the relative strength and (rho) over bar is the relative density, for tension is 1.09 and for compression is 0.63. Both exponents are lower than the value of 1.5 in the Gibson-Ashby model that describes the relation between relative strength and relative density where the strength of constituent material is independent of external size, which indicates that strength of constituent ZnO increases as wall thickness decreases. We find, based on hole-nanoindentation and glazing incidence X-ray diffraction, that this wall-thickness-dependent strength of nanotubular ZnO is not caused by strengthening of constituent ZnO by size reduction at the nanoscale. Finite element analysis suggests that the wall-thickness-dependent strength of nanotubular ZnO originates from nanotubular structures formed on ligaments of np-Au

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

Nonmonotone Barzilai-Borwein Gradient Algorithm for ℓ1\ell_1-Regularized Nonsmooth Minimization in Compressive Sensing

This paper is devoted to minimizing the sum of a smooth function and a nonsmooth $\ell_1$-regularized term. This problem as a special cases includes the $\ell_1$-regularized convex minimization problem in signal processing, compressive sensing, machine learning, data mining, etc. However, the non-differentiability of the $\ell_1$-norm causes more challenging especially in large problems encountered in many practical applications. This paper proposes, analyzes, and tests a Barzilai-Borwein gradient algorithm. At each iteration, the generated search direction enjoys descent property and can be easily derived by minimizing a local approximal quadratic model and simultaneously taking the favorable structure of the $\ell_1$-norm. Moreover, a nonmonotone line search technique is incorporated to find a suitable stepsize along this direction. The algorithm is easily performed, where the values of the objective function and the gradient of the smooth term are required at per-iteration. Under some conditions, the proposed algorithm is shown to be globally convergent. The limited experiments by using some nonconvex unconstrained problems from CUTEr library with additive $\ell_1$-regularization illustrate that the proposed algorithm performs quite well. Extensive experiments for $\ell_1$-regularized least squares problems in compressive sensing verify that our algorithm compares favorably with several state-of-the-art algorithms which are specifically designed in recent years.Comment: 20 page

Infinitesimal sulfur fusion yields quasi-metallic bulk silicon for stable and fast energy storage

A fast-charging battery that supplies maximum energy is a key element for vehicle electrification. High-capacity silicon anodes offer a viable alternative to carbonaceous materials, but they are vulnerable to fracture due to large volumetric changes during charge???discharge cycles. The low ionic and electronic transport across the silicon particles limits the charging rate of batteries. Here, as a three-in-one solution for the above issues, we show that small amounts of sulfur doping (&lt;1 at%) render quasi-metallic silicon microparticles by substitutional doping and increase lithium ion conductivity through the flexible and robust self-supporting channels as demonstrated by microscopy observation and theoretical calculations. Such unusual doping characters are enabled by the simultaneous bottom-up assembly of dopants and silicon at the seed level in molten salts medium. This sulfur-doped silicon anode shows highly stable battery cycling at a fast-charging rate with a high energy density beyond those of a commercial standard anode