Nanovectors for anticancer agents based on superparamagnetic iron oxide nanoparticles

During the last decade, the application of nanotechnologies for anticancer drug delivery has been extensively explored, hoping to improve the efficacy and to reduce side effects of chemotherapy. The present review is dedicated to a certain kind of anticancer drug nanovectors developed to target tumors with the help of an external magnetic field. More particularly, this work treats anticancer drug nanoformulations based on superparamagnetic iron oxide nanoparticles coated with biocompatible polymers. The major purpose is to focus on the specific requirements and technological difficulties related to controlled delivery of antitumoral agents. We attempt to state the problem and its possible perspectives by considering the three major constituents of the magnetic therapeutic vectors: iron oxide nanoparticles, polymeric coating and anticancer drug.     

Incorporating social preferences into the ecological limits of hydrologic alteration (ELOHA): a case study in the Yampa‐White River basin,Colorado

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Cellular and molecular basis of decision‐making

People think they are in control of their own decisions: what to eat or drink, whom to marry or pick a fight with, where to live, what to buy. Behavioural economists and neurophysiologists have long studied decision‐making behaviours. However, these behaviours have only recently been studied through the light of molecular genetics. Here, we review recent research in mice, Drosophila melanogaster and Caenorhabditis elegans, that analyses the molecular and cellular mechanisms underlying decision‐making. These studies interrogate decision‐making about food, sexual behaviour, aggression or foraging strategies, and add molecular and cell biology understanding onto the consilience of brain and decision.     

Fluorescent sensors based on quinoline‐containing styrylcyanine: determination of ferric ions,hydrogen peroxide,and glucose,pH‐sensitive properties and bioimaging

A novel styrylcyanine‐based fluorescent probe 1 was designed and synthesized via facile methods. Ferric ions quenched the fluorescence of probe 1, whereas the addition of ferrous ions led to only small changes in the fluorescence signal. When hydrogen peroxide was introduced into the solution containing probe 1 and Fe²⁺, Fe²⁺ was oxidized to Fe³⁺, resulting in the quenching of the fluorescence. The probe 1/Fe²⁺ solution fluorescence could also be quenched by H₂O₂ released from glucose oxidation by glucose oxidase (GOD), which means that probe 1/Fe²⁺ platform could be used to detect glucose. Probe 1 is fluorescent in basic and neutral media but almost non‐fluorescent in strong acidic environments. Such behaviour enables it to work as a fluorescent pH sensor in both the solution and solid states and as a chemosensor for detecting volatile organic compounds with high acidity and basicity. Subsequently, the fluorescence microscopic images of probe 1 in live cells and in zebrafish were achieved successfully, suggesting that the probe has good cell membrane permeability and a potential application for imaging in living cells and living organisms. Copyright © 2014 John Wiley & Sons, Ltd.     

Strategies for cellular decision‐making

Stochasticity pervades life at the cellular level. Cells receive stochastic signals, perform detection and transduction with stochastic biochemistry, and grow and die in stochastic environments. Here we review progress in going from the molecular details to the information‐processing strategies cells use in their decision‐making. Such strategies are fundamentally influenced by stochasticity. We argue that the cellular decision‐making can only be probabilistic and occurs at three levels. First, cells must infer from noisy signals the probable current and anticipated future state of their environment. Second, they must weigh the costs and benefits of each potential response, given that future. Third, cells must decide in the presence of other, potentially competitive, decision‐makers. In this context, we discuss cooperative responses where some individuals can appear to sacrifice for the common good. We believe that decision‐making strategies will be conserved, with comparatively few strategies being implemented by different biochemical mechanisms in many organisms. Determining the strategy of a decision‐making network provides a potentially powerful coarse‐graining that links systems and evolutionary biology to understand biological design.     

A GIS‐based decision‐making approach for prioritizing seabird management following predator eradication

Given that 29% of seabird species are threatened with extinction, protecting seabird colonies on offshore islands is a global conservation priority. Seabirds are vulnerable to non‐native predator invasions, which reduce or eliminate colonies. Accordingly, conservation efforts have focused on predator eradication. However, affected populations are often left to passively recover following eradications. Although seabirds are highly mobile, their life history traits such as philopatry can limit passive recolonization of newly predator‐free habitat. In such cases, seabird colonies can potentially be re‐instated with active restoration via chick translocations or social attraction methods, which can be risky and expensive. We used biogeographic and species‐specific behavioral data in the Hauraki Gulf, New Zealand, a global hotspot of seabird diversity and predator eradications, to illustrate the use of geographic information systems multi‐criteria decision analysis to prioritize islands for active seabird restoration. We identified nine islands with low observed passive recovery of seabirds posteradication over a 50‐year timeframe, and classified these as sites where active seabird management could be prioritized. Such spatially explicit tools are flexible, allowing for managers to choose case‐specific criteria such as time, funding, and goals constrained for their conservation needs. Furthermore, this flexibility can also be applied to threatened species management by customizing the decision criteria for individual species' capacity to passively recolonize islands. On islands with complex restoration challenges, decision tools that help island restoration practitioners decide whether active seabird management should be paired with eradication can optimize restoration outcomes and ecosystem recovery.     

Life‐cycle and cost of goods assessment of fed‐batch and perfusion‐based manufacturing processes for mAbs

Life‐cycle assessment (LCA) is an environmental assessment tool that quantifies the environmental impact associated with a product or a process (e.g., water consumption, energy requirements, and solid waste generation). While LCA is a standard approach in many commercial industries, its application has not been exploited widely in the bioprocessing sector. To contribute toward the design of more cost‐efficient, robust and environmentally‐friendly manufacturing process for monoclonal antibodies (mAbs), a framework consisting of an LCA and economic analysis combined with a sensitivity analysis of manufacturing process parameters and a production scale‐up study is presented. The efficiency of the framework is demonstrated using a comparative study of the two most commonly used upstream configurations for mAb manufacture, namely fed‐batch (FB) and perfusion‐based processes. Results obtained by the framework are presented using a range of visualization tools, and indicate that a standard perfusion process (with a pooling duration of 4 days) has similar cost of goods than a FB process but a larger environmental footprint because it consumed 35% more water, demanded 17% more energy, and emitted 17% more CO₂ than the FB process. Water consumption was the most important impact category, especially when scaling‐up the processes, as energy was required to produce process water and water‐for‐injection, while CO₂ was emitted from energy generation. The sensitivity analysis revealed that the perfusion process can be made more environmentally‐friendly than the FB process if the pooling duration is extended to 8 days. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1324–1335, 2016     

Cascading tripartite binomial classification plans to monitor European red mite (Acari,Tetranychidae) through a season; development and evaluation of a new methodology for pest monitoring

A monitoring protocol that schedules future sample bouts based on the outcome of density classification and expected population growth has been developed and applied to monitoring European red mite (Panonychus ulmi Koch) through a growing season. The monitoring protocol is based on concatenating through time tripartite sequential classification sampling plans that use binomial counts in lieu of complete enumeration. Binomial counts are scored positive when the number of organisms (mites) on a sample unit (leaves) exceeds a tally number. At each sample occasion the monitoring procedure leads to one of three possible decisions; intervene when the density is high, sample at the next sample occasion (after one week) when the density is intermediate, and sample at the second next sample occasion (after two weeks) when the density is low. Evaluation of the monitoring protocol under field conditions showed that the protocol with constituent tally 0 binomial count sampling plans was quite successful in timing intervention at the moment when population densities were about to exceed an established threshold that dictated intervention. The performance of this monitoring protocol and another protocol in which constituent sampling plans used binomial counts with a tally number of 4 were compared using simulation. Sampling plans that used a tally number of 4 were more precise than plans that used a tally number of 0. However, the overall performance of the monitoring protocol based on tally 0 sampling plans did not greatly differ from the monitoring protocol based on tally 4 sampling plans. Simulated performance of the tally 0 protocol was corroborated by field evaluation. The monitoring protocol based on tripartite classification required 30 to 45 percent fewer sample bouts than a protocol based on conventional sequential classification at weekly intervals. The monitoring protocol based on tripartite classification was also better able to schedule intervention when needed compared to a protocol based on conventional classification at two week intervals. Using the tally 0 protocol and current thresholds forP. ulmi, cumulative mite density was kept below 300 mite-days per leaf, which is well below levels regarded damaging. A tally 0 protocol with raised thresholds, developed on the basis of this finding, gave the best simulated performance of all protocols evaluated.     

The combined effects of the 5‐ HTTLPR and HTR1A rs6295 polymorphisms modulate decision making in schizophrenia patients

Decision making ability has been reported to be impaired in schizophrenia patients, but no research has examined the genetic bases of this impairment. This study investigated how decision making was affected by the genetic variants in the serotonin transporter gene (triallelic 5‐ HTTLPR) and serotonin receptor 1A gene (rs6295) and their interaction in 465 schizophrenia patients and 448 healthy controls. The Iowa Gambling Task (IGT) was used to evaluate decision making under ambiguity (the first 40 trials) and decision making under risk (the last 60 trials). Results showed that, among the patients, the main effects of 5‐ HTTLPR (F_2,16 = 6.54, P = 0.002) and HTR1A rs6295 (F_2,16 = 3.87, P = 0.021) polymorphisms and their interaction effect (F_4,16 = 3.32, P = 0.005) were significant for the first 40 trials, with the GG genotype of HTR1A rs6295, the L′L′ genotype of 5‐ HTTLPR and the GG‐L′L′ combination showing poorer IGT performance than their counterparts. Results for the healthy controls showed a similar pattern but did not reach statistical significance. No significant effects were found for the last 60 trials. These results are discussed in terms of their implications for our understanding of the genetic mechanisms of decision making in schizophrenia patients as well as healthy adults.     

Decision‐making under climatic uncertainty: A case study involving an Australian Ramsar‐listed wetland

Summary Conservation management in agricultural landscapes involves identification and prioritization of assets, and interventions to reverse or arrest decline. Planning requires synthesis of hydrological, ecological and agronomic information and intuitions. We provide a case study involving the Lake Warden Wetland System, a Ramsar‐listed site on the south coast of Western Australia threatened by salinity and flooding. As the relative merits of management options (including engineering‐based solutions and catchment revegetation) may be sensitive to climate change, we captured our knowledge and understanding of the effectiveness of options under different climate change scenarios using Bayesian belief networks. We insulated against overconfidence by an info‐gap analysis that describes the trade‐off between aspiration and immunity to uncertainty. Only engineering‐based solutions offer reasonable prospects for achieving stated conservation goals in the Lake Warden Wetland System within a 25‐year time horizon. Marginal gains derived from co‐investment in revegetation varied among the assets. We advocate explicit treatment of uncertainty and risk‐based approaches to decision‐making to equip managers with a means of progressing conservation goals. The complementary insights offered by Bayesian belief networks and info‐gap analysis provide a sound basis for managers to assess the extent to which candidate management actions are robust to uncertainty.     

Effect of species‐counting protocols and the spatial distribution of effort on rarefaction curves in relation to decision making in environmental‐impact assessments

Rarefaction Curves are frequently used in Environmental Impact Assessments to evaluate sampling sufficiency, but without clear guidelines of how to ensure that the assumptions of the methods are met. Infrastructure projects in the Brazilian Amazon and elsewhere often occupy extensive areas in remote locations with difficult access, and random sampling under such conditions is impractical. We tested the influence of sampling unit (sample or individual), and geographic distance between samples on rarefaction curve s, and evaluated the magnitude of errors resulting from the misuse of rarefaction curve in decision making, using frogs from four Amazonian sampling sites. Individual‐based rarefaction curve were steeper than those generated by sample‐based rarefaction curve. Geographic distance influenced the number of exclusive species in a predictable fashion only in one area, and not in the Environmental Impact Assessment site. Misuse of rarefaction curve generated large errors in the identification of vulnerable taxa. Because the rarefaction curve model is sensitive to the assumption of randomness and geographic distance can influence it unpredictably, we suggest that rarefaction curve should generally not be used to estimate sample completeness when making management decisions for environmental licensing purposes.     

Synthesis of novel imidazopyridines and their biological evaluation as potent anticancer agents: A promising candidate for glioblastoma

Novel imidazopyridine derivatives were synthesized according to a very simple protocol and then subjected to cytotoxicity testing against LN-405 cells. Two of the compounds exhibited antiproliferative effects on LN-405 cells at 10 and 75?µM and were selected as lead compounds for further study. Safety experiment for lead compounds on WS1 was carried out and IC₅₀ values were calculated as 480 and 844?µM. LN-405 cell line were incubated with the lead compounds and then tested for DNA damage by comet assay and effects on cell cycle using flow cytometry. The results of these two tests showed that both lead compounds affected the G0/G1 phase and did not allow the cells to reach the synthesis phase. The log BB (blood–brain barrier) and Caco-2 permeability of the synthesized molecules were calculated and it was shown that imidazopyridine derivatives taken orally are likely to pass through gastrointestinal membrane and the blood–brain barrier.     

Decision‐Making Deficits and Overeating: A Risk Model for Obesity

Objective: To demonstrate that human overeating is not just a passive response to salient environmental triggers and powerful physiological drives; it is also about making choices. The ventromedial prefrontal cortex has been strongly implicated in the neural circuitry necessary for making advantageous decisions when various options for action are available. Decision‐making deficits have been found in patients with ventromedial prefrontal cortex lesions and in those with substance dependence—impairments that reflect an inability to advantageously assess future consequences. That is, they choose immediate rewards in the face of future long‐term negative consequences. Research Methods and Procedures: We extended this research to the study of overeating and overweight, testing a regression model that predicted that poor decision making (as assessed by a validated computerized gambling task) and a tendency to overeat under stress would correlate with higher BMI in a group of healthy adult women (N = 41) representing a broad range of body weights. Results: We found statistically significant main effects for both independent variables in the predicted direction (p < 0.05; R² = 0.35). Indeed, the decision‐making impairments across the 100 trials of the computer task were greater in those with high BMI than in previous studies with drug addicts. Discussion: Findings suggested that cortical and subcortical processes, which regulate one's ability to inhibit short‐term rewards when the long‐term consequences are deleterious, may also influence eating behaviors in a culture dominated by so many, and such varied, sources of palatable and calorically dense sources of energy.     

Ecological restoration should be redefined for the twenty‐first century

Forty years ago, ecological restoration was conceptualized through a natural science lens. Today, ecological restoration has evolved into a social and scientific concept. The duality of ecological restoration is acknowledged in guidance documents on the subject but is not apparent in its definition. Current definitions reflect our views about what ecological restoration does but not why we do it. This viewpoint does not give appropriate credit to contributions from social sciences, nor does it provide compelling goals for people with different motivating rationales to engage in or support restoration. In this study, I give a concise history of the conceptualization and definition of ecological restoration, and I propose an alternative definition and corresponding viewpoint on restoration goal‐setting to meet twenty‐first century scientific and public inquiry.     

A framework for the identification of promising bio‐based chemicals

Recent progress in metabolic engineering and synthetic biology enables the use of microorganisms for the production of chemicals—“bio‐based chemicals.” However, it is still unclear which chemicals have the highest economic prospect. To this end, we develop a framework for the identification of such promising ones. Specifically, we first develop a genome‐scale constraint‐based metabolic modeling approach, which is used to identify a candidate pool of 209 chemicals (together with the estimated yield, productivity, and residence time for each) from the intersection of the high‐production‐volume chemicals and the KEGG and MetaCyc databases. Second, we design three screening criteria based on a chemical’s profit margin, market volume, and market size. The total process cost, including the downstream separation cost, is systematically incorporated into the evaluation. Third, given the three aforementioned criteria, we identify 32 products as economically promising if the maximum yields can be achieved, and 22 products if the maximum productivities can be achieved. The breakeven titer that renders zero profit margin for each product is also presented. Comparisons between extracellular and intracellular production, as well as Escherichia coli and Saccharomyces cerevisiae systems are also discussed. The proposed framework provides important guidance for future studies in the production of bio‐based chemicals. It is also flexible in that the databases, yield estimations, and criteria can be modified to customize the screening.     

Possible use of Biolog methodology for monitoring yeast presence in alcoholic fermentation for wine‐making

Aims: A research was undertaken to explore the possibility to use Biolog system of microbial metabolic characterization for the monitoring of yeast population evolution during alcoholic fermentation for wine production. Methods and Results: An application of Biolog system was employed for the characterization of yeasts of oenological interest, in pure cultures and mixed consortia, in various cell concentrations. The system’s capacity to discriminate among different cell concentrations of the same yeast strain was ascertained, along with the capacity to discriminate between mixed and pure populations. Conclusions: The tested application of Biolog system resulted suitable for a quick recognition (24 h) of the presence of starter cultures within mixed populations of autochthonous yeasts. Such discrimination was confirmed with the one resulting from molecular techniques. Significance and Impact of the Study: The study suggests the possibility to employ Biolog system for an early monitoring of yeast evolution in modern wine‐making fermentations, where specialized yeasts are more and more frequently used as starters and their ability to overcome autochthonous yeast populations is crucial.     

Bionanoconjugate‐based composites for decontamination of nerve agents

We have developed enzyme‐based composites that rapidly and effectively detoxify simulants of V‐ and G‐type chemical warfare nerve agents. The approach was based on the efficient immobilization of organophosphorus hydrolase onto carbon nanotubes to form active and stable conjugates that were easily entrapped in commercially available paints. The resulting catalytic‐based composites showed no enzyme leaching and rendered >99% decontamination of 10 g/m² paraoxon, a simulant of the V‐type nerve agent, in 30 minutes and >95% decontamination of diisopropylfluorophosphate, a simulant of G‐type nerve agent, in 45 minutes. The formulations are expected to be environmentally friendly and to offer an easy to use, on demand, decontamination alternative to chemical approaches for sustainable material self‐decontamination. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

An agent‐based modelling approach to evaluate factors influencing bioenergy crop adoption in north‐east Scotland

An agent‐based modelling (ABM) framework was adapted to assess bioenergy crop uptake and integrate social and economic processes with biophysical elements. Survey results indicated that economic rationalisation was intrinsic to farmers’ decision‐making, but was not the only consideration. This study presents an approach, set within an established resource management framework, to incorporate a number of key socio‐economic factors, which we call Mitigation Willingness Factors (MWFs), using survey data collected from farmers and land managers, into the ABM. The MWFs represent farmers’ willingness to compromise revenue in order to reduce GHG emissions, derived from their attitudes to climate change and the ability of different economic mechanisms to stimulate energy crop uptake. Adoption of bioenergy crops of different farmer types and farming enterprises was also assessed. Adoption rates and scenarios that take into account noneconomic factors are presented, and particular farming enterprises that may respond more positively to policy initiatives are identified.     

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.