Alternative low-cost adsorbent for water and wastewater decontamination derived from eggshellwaste: an overview

As the current global trend towards more stringent environmental standards, technical applicability and cost-effectiveness became key factors in the selection of adsorbents for water and wastewater treatment. Recently, various low-cost adsorbents derived from agricultural waste, industrial by-products or natural materials, have been intensively investigated. In this respect, the eggshells from egg-breaking operations constitute significant waste disposal problems for the food industry, so the development of value-added by-products from this waste is to be welcomed. The egg processing industry is very competitive, with low profit margins due to global competition and cheap imports. Additionally, the costs associated with the egg shell disposal (mainly on landfill sites) are significant, and expected to continue increasing as landfill taxes increase. The aim of the present review is to provide an overview on the development of low-cost adsorbents derived from eggshell by-products

Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors

Enteric dysbiosis and fecal calprotectin expression in premature infants.

BackgroundPremature infants often develop enteric dysbiosis with a preponderance of Gammaproteobacteria, which has been related to adverse clinical outcomes. We investigated the relationship between increasing fecal Gammaproteobacteria and mucosal inflammation, measured by fecal calprotectin (FC).MethodsStool samples were collected from very-low-birth weight (VLBW) infants at ≤2, 3, and 4 weeks' postnatal age. Fecal microbiome was surveyed using polymerase chain reaction amplification of the V4 region of 16S ribosomal RNA, and FC was measured by enzyme immunoassay.ResultsWe enrolled 45 VLBW infants (gestation 27.9 ± 2.2 weeks, birth weight 1126 ± 208 g) and obtained stool samples at 9.9 ± 3, 20.7 ± 4.1, and 29.4 ± 4.9 days. FC was positively correlated with the genus Klebsiella (r = 0.207, p = 0.034) and its dominant amplicon sequence variant (r = 0.290, p = 0.003), but not with the relative abundance of total Gammaproteobacteria. Klebsiella colonized the gut in two distinct patterns: some infants started with low Klebsiella abundance and gained these bacteria over time, whereas others began with very high Klebsiella abundance.ConclusionIn premature infants, FC correlated with relative abundance of a specific pathobiont, Klebsiella, and not with that of the class Gammaproteobacteria. These findings indicate a need to define dysbiosis at genera or higher levels of resolution

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made both neurobiologically more plausible and computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, for example, fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of recurrent neural networks may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics

The Role of Lactic Acid Adsorption by Ion Exchange Chromatography

Background: The polyacrylic resin Amberlite IRA-67 is a promising adsorbent for lactic acid extraction from aqueous solution, but little systematic research has been devoted to the separation efficiency of lactic acid under different operating conditions. Methodology/Principal Findings: In this paper, we investigated the effects of temperature, resin dose and lactic acid loading concentration on the adsorption of lactic acid by Amberlite IRA-67 in batch kinetic experiments. The obtained kinetic data followed the pseudo-second order model well and both the equilibrium and ultimate adsorption slightly decreased with the increase of the temperature at 293–323K and 42.5 g/liter lactic acid loading concentration. The adsorption was a chemically heterogeneous process with a mean free energy value of 12.18 kJ/mol. According to the Boyd _ plot, the lactic acid uptake process was primarily found to be an intraparticle diffusion at a lower concentration (,50 g/liter) but a film diffusion at a higher concentration (.70 g/liter). The values of effective diffusion coefficient D i increased with temperature. By using our Equation (21), the negative values of DGu and DHu revealed that the adsorption process was spontaneous and exothermic. Moreover, the negative value of DSu reflected the decrease of solid-liquid interface randomness at the solid-liquid interface when adsorbing lactic acid on IRA-67. Conclusions/Significance: With the weakly basic resin IRA-67, in situ product removal of lactic acid can be accomplishe

Serum calcium and incident diabetes: an observational study and meta-analysis

SUMMARY: The study aimed to prospectively evaluate if serum calcium is related to diabetes incidence in Hong Kong Chinese. The results showed that serum calcium has a significant association with increased risk of diabetes. The result of meta-analysis reinforced our findings. INTRODUCTION: This study aimed to evaluate the association of serum calcium, including serum total calcium and albumin-corrected calcium, with incident diabetes in Hong Kong Chinese. METHODS: We conducted a retrospective cohort study in 6096 participants aged 20 or above and free of diabetes at baseline. Serum calcium was measured at baseline. Incident diabetes was determined from several electronic databases. We also searched relevant databases for studies on serum calcium and incident diabetes and conducted a meta-analysis using fixed-effect modeling. RESULTS: During 59,130.9 person-years of follow-up, 631 participants developed diabetes. Serum total calcium and albumin-corrected calcium were associated with incident diabetes in the unadjusted model. After adjusting for demographic and clinical variables, the association remained significant only for serum total calcium (hazard ratio (HR), 1.32 (95 % confidence interval (CI), 1.02-1.70), highest vs. lowest quartile). In a meta-analysis of four studies including the current study, both serum total calcium (pooled risk ratio (RR), 1.38 (95 % CI, 1.15-1.65); I (2) = 5 %, comparing extreme quantiles) and albumin-corrected calcium (pooled RR, 1.29 (95 % CI, 1.03-1.61); I (2) = 0 %, comparing extreme quantiles) were associated with incident diabetes. Penalized regression splines showed that the association of incident diabetes with serum total calcium and albumin-correlated calcium was non-linear and linear, respectively. CONCLUSIONS: Elevated serum calcium concentration is associated with incident diabetes. The mechanism underlying this association warrants further investigation

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability