Investigating the Parameters Affecting the Stability of Superparamagnetic Iron Oxide-Loaded Nanoemulsion Using Artificial Neural Networks

Nanoemulsions are increasingly being investigated for their fascinating capability of loading both hydrophobic and hydrophilic molecules while their stability is still an issue, being affected by various factors. In this study, to evaluate the dominant factors affecting the stability of nanoemulsions, artificial neural networks (ANNs) were implemented. Nanoemulsions of almond oil in water containing oleic acid-coated superparamagnetic iron oxide nanoparticles were prepared using a mixture of Tween 80 and Span 80 as surfactant system and ethanol as a co-surfactant. The ratio of transparency of the samples at 30 min and 7 days after preparation was taken as an indication of the stability of samples. Four independent variables, namely, concentration of nanoparticle, surfactant, oil, and alcohol were investigated to find their relations with the dependent variable (i.e., transparency ratio). Using ANNs modeling, it was concluded that the stability is affected by all variables, with all variables showing reverse effect on the stability beyond an optimum amount.Key words: artificial neural networks, nanoemulsion, optimization, stability, superparamagnetic iron oxide     

Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks

We describe here the application of a type of artificial neural network, the Gaussian radial basis function (RBF) network, in the identification of a large number of phytoplankton strains from their 11-dimensional flow cytometric characteristics measured by the European Optical Plankton Analyser instrument. The effect of network parameters on optimization is examined. Optimized RBF networks recognized 34 species of marine and freshwater phytoplankton with 91.5% success overall. The relative importance of each measured parameter in discriminating these data and the behavior of RBF networks in response to data from “novel” species (species not present in the training data) were analyzed.     

Crystal structure of a trimeric form of the KV7.1 (KCNQ1) A‐domain tail coiled‐coil reveals structural plasticity and context dependent changes in a putative coiled‐coil trimerization motif

Coiled-coils are widespread protein–protein interaction motifs typified by the heptad repeat (abcdefg)_n in which “a” and “d” positions are hydrophobic residues. Although identification of likely coiled-coil sequences is robust, prediction of strand order remains elusive. We present the X-ray crystal structure of a short form (residues 583–611), “Q1-short,” of the coiled-coil assembly specificity domain from the voltage-gated potassium channel Kv7.1 (KCNQ1) determined at 1.7 Å resolution. Q1-short lacks one and half heptads present in a previously studied tetrameric coiled-coil construct, Kv7.1 585–621, “Q1-long.” Surprisingly, Q1-short crystallizes as a trimer. In solution, Q1-short self-assembles more poorly than Q1-long and depends on an R-h-x-x-h-E motif common to trimeric coiled-coils. Addition of native sequences that include “a” and “d” positions C-terminal to Q1-short overrides the R-h-x-x-h-E motif influence and changes assembly state from a weakly associated trimer to a strongly associated tetramer. These data provide a striking example of a naturally occurring amino sequence that exhibits context-dependent folding into different oligomerization states, a three-stranded versus a four-stranded coiled-coil. The results emphasize the degenerate nature of coiled-coil energy landscapes in which small changes can have drastic effects on oligomerization. Discovery of these properties in an ion channel assembly domain and prevalence of the R-h-x-x-h-E motif in coiled-coil assembly domains of a number of different channels that are thought to function as tetrameric assemblies raises the possibility that such sequence features may be important for facilitating the assembly of intermediates en route to the final native state.     

Auditing the management of vaccine-preventable disease outbreaks: the need for a tool

Public health activities, especially infectious disease control, depend on effective teamwork. We present the results of a pilot audit questionnaire aimed at assessing the quality of public health services in the management of VPD outbreaks. Audit questionnaire with three main areas indicators (structure, process and results) was developed. Guidelines were set and each indicator was assessed by three auditors. Differences in indicator scores according to median size of outbreaks were determined by ANOVA (significance at p≤0.05). Of 154 outbreaks; eighteen indicators had a satisfactory mean score, indicator “updated guidelines” and “timely reporting” had a poor mean score (2.84±106 and 2.44±1.67, respectively). Statistically significant differences were found according to outbreak size, in the indicators “availability of guidelines/protocol updated less than 3 years ago” (p = 0.03) and “days needed for outbreak control” (p = 0.04). Improving availability of updated guidelines, enhancing timely reporting and adequate recording of control procedures taken is needed to allow for management assessment and improvement.     

Applications of neural networks to recovery of biological products

Artificial neural networks (ANN) are being applied to recovery of products from fermentation broths. Recovery methods for which mathematical models are complex or non-existent are particularly suitable for control and analysis by ANNs. Use and potential of artificial neural networks for product recovery applications are reviewed.     

What are artificial neural networks and what they can do?

The artificial neural networks (ANN) are very often applied in many areas of toxicology for the solving of complex problems, such as the prediction of chemical compound properties and quantitative structure-activity relationship. The aim of this contribution is to give the basic knowledge about conception of ANN, theirs division and finally, the typical application of ANN will be discussed. Due to the diversity of architectures and adaptation algorithms, the ANNs are used in the broad spectrum of applications from the environmental processes modeling, through the optimization to quantitative structure-activity relationship (QSAR) methods. In addition, especially ANNs with Kohonen learning are very effective classification tool. The ANNs are mostly applied in cases, where the commonly used methods does not work.     

Fish distribution predictions from different points of view: comparing associative neural networks,geostatistics and regression models

Accurate prediction of species distributions based on sampling and environmental data is essential for further scientific analysis, such as stock assessment, detection of abundance fluctuation due to climate change or overexploitation, and to underpin management and legislation processes. The evolution of computer science and statistics has allowed the development of sophisticated and well-established modelling techniques as well as a variety of promising innovative approaches for modelling species distribution. The appropriate selection of modelling approach is crucial to the quality of predictions about species distribution. In this study, modelling techniques based on different approaches are compared and evaluated in relation to their predictive performance, utilizing fish density acoustic data. Generalized additive models and mixed models amongst the regression models, associative neural networks (ANNs) and artificial neural networks ensemble amongst the artificial neural networks and ordinary kriging amongst the geostatistical techniques are applied and evaluated. A verification dataset is used for estimating the predictive performance of these models. A combination of outputs from the different models is applied for prediction optimization to exploit the ability of each model to explain certain aspects of variation in species acoustic density. Neural networks and especially ANNs appear to provide more accurate results in fitting the training dataset while generalized additive models appear more flexible in predicting the verification dataset. The efficiency of each technique in relation to certain sampling and output strategies is also discussed.     

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.     

Tuning genetic clocks employing DNA binding sites

Periodic oscillations play a key role in cell physiology from the cell cycle to circadian clocks. The interplay of positive and negative feedback loops among genes and proteins is ubiquitous in these networks. Often, delays in a negative feedback loop and/or degradation rates are a crucial mechanism to obtain sustained oscillations. How does nature control delays and kinetic rates in feedback networks? Known mechanisms include proper selection of the number of steps composing a feedback loop and alteration of protease activity, respectively. Here, we show that a remarkably simple means to control both delays and effective kinetic rates is the employment of DNA binding sites. We illustrate this design principle on a widely studied activator-repressor clock motif, which is ubiquitous in natural systems. By suitably employing DNA target sites for the activator and/or the repressor, one can switch the clock “on” and “off” and precisely tune its period to a desired value. Our study reveals a design principle to engineer dynamic behavior in biomolecular networks, which may be largely exploited by natural systems and employed for the rational design of synthetic circuits.     

Artificial neural networks for decision-making in urologic oncology

Artificial neural networks (ANNs) are computational methodologies that perform multifactorial analyses, inspired by networks of biological neurons. Like neural networks, ANNs contain layers of simple points (nodes) of data that interract through carefully weighted connection lines. ANNs are "trained" and balanced by having been previously fed data, which the ANN uses as the means for adjusting its interconnections. Studies have shown that novel and highly accurate ANNs significantly enhance the ability to detect prostate cancer early (high sensitivity) while avoiding a greater number of unnecessary tissue samplings (high specificity). The use of ANNs in prostate cancer is ideal because of 1) multiple predicting factors that influence outcome; 2) the desire to offer individual consulting based on various tests; 3) the fact that prior logistic regression analysis results have had serious limitations in application; and 4) the need for an up-to-date tool that can apply easily to everyone. An ANN should be seen as an important tool that is complementary to the physician's personal knowledge and judgment in making decisions.     

Size Control in the Nanoprecipitation Process of Stable Iodine (127I) Using Microchannel Reactor—Optimization by Artificial Neural Networks

In this study, nanosuspension of stable iodine (¹²⁷I) was prepared by nanoprecipitation process in microfluidic devices. Then, size of particles was optimized using artificial neural networks (ANNs) modeling. The size of prepared particles was evaluated by dynamic light scattering. The response surfaces obtained from ANNs model illustrated the determining effect of input variables (solvent and antisolvent flow rate, surfactant concentration, and solvent temperature) on the output variable (nanoparticle size). Comparing the 3D graphs revealed that solvent and antisolvent flow rate had reverse relation with size of nanoparticles. Also, those graphs indicated that the solvent temperature at low values had an indirect relation with size of stable iodine (¹²⁷I) nanoparticles, while at the high values, a direct relation was observed. In addition, it was found that the effect of surfactant concentration on particle size in the nanosuspension of stable iodine (¹²⁷I) was depended on the solvent temperature.

Graphical Abstract

Open in a separate windowNanoprecipitation process of stable iodine (127I) and optimization of particle size using ANNs modeling.KEY WORDS: ANNs, microfluidic, nanoprecipitation, particle size, stable iodine     

Rheological Characterization of Cataplasm Bases Composed of Cross-Linked Partially Neutralized Polyacrylate Hydrogel

Viscoelasticity is a useful parameter for characterizing the intrinsic properties of the cross-linked polyacrylate hydrogel used in cataplasm bases. The aim of this study was to investigate the effects of various formulation parameters on the rheological characteristics of polyacrylate hydrogel. The hydrogel layers were formed using a partially neutralized polyacrylate (Viscomate^™), which contained acrylic acid and sodium acrylate in different copolymerization ratios, as the cross-linked gel framework. Dihydroxyaluminum aminoacetate (DAAA), which produces aluminum ions, was used as the cross-linking agent. Rheological analyses were performed using a “stress amplitude sweep” and a “frequency sweep”. The results showed that greater amounts of acrylic acid in the structure of Viscomate as well as higher concentrations of DAAA and Viscomate led to an increase in the elastic modulus (G′). However, greater amounts of acrylic acid in the structure of Viscomate and higher concentrations of DAAA had an opposite on the viscous modulus (G″); this might be owing to higher steric hindrance. The results of this study can serve as guidelines for the optimization of formulations for cataplasms.KEY WORDS: cataplasm, hardness, rheology, viscoelasticity     

Methodological Rigour and Transparency of Clinical Practice Guidelines Developed by Neurology Professional Societies in Croatia

Background

Clinical practice guidelines are systematically created documents that summarize knowledge and assist in delivering high-quality medicine by identifying evidence that supports best clinical care. They are produced not only by international professional groups but also by local professionals to address locally-relevant clinical practice. We evaluated the methodological rigour and transparency of guideline development in neurology formulated by professionals in a local medical community.

Methods

We analyzed clinical guidelines in neurology publicly available at the web-site of the Physicians’ Assembly in Croatia in 2012: 6 guidelines developed by Croatian authors and 1 adapted from the European Federation of Neurological Societies. The quality was assessed by 2 independent evaluators using the AGREE II instrument. We also conducted a search of the Cochrane Library to identify potential changes in recommendation from Cochrane systematic reviews included in guideline preparation.

Results

The methodological quality of the guidelines greatly varied across different domains. „Scope and Purpose” and „Clarity of Presentation“ domains received high scores (100% [95% confidence interval (CI) 98.5–100] and 97% [77.9–100], respectively), the lowest scores were in “Stakeholder Involvement“ (19% [15.5–34.6]) and “Editorial Independence” (0% [0–19.2]). Conclusions of 3 guidelines based on Cochrane systematic reviews were confirmed in updated versions and one update provided new information on the effectiveness of another antidepressant. Two Cochrane reviews used in guidelines were withdrawn and split into new reviews and their findings are now considered to be out of date.

Conclusion

Neurological guidelines used in Croatia differ in structure and their methodological quality. We recommend to national societies and professional groups to develop a more systematic and rigorous approach to the development of the guidelines, timely inclusion of best evidences and an effort to involve target users and patients in the guideline development procedures.     

Use of Artificial Neural Networks To Accurately Identify Cryptosporidium Oocyst and Giardia Cyst Images

Cryptosporidium parvum and Giardia lamblia are protozoa capable of causing gastrointestinal diseases. Currently, these organisms are identified using immunofluorescent antibody (IFA)-based microscopy, and identification requires trained individuals for final confirmation. Since artificial neural networks (ANN) can provide an automated means of identification, thereby reducing human errors related to misidentification, ANN were developed to identify Cryptosporidium oocyst and Giardia cyst images. Digitized images of C. parvum oocysts and G. lamblia cysts stained with various commercial IFA reagents were used as positive controls. The images were captured using a color digital camera at 400× (total magnification), processed, and converted into a binary numerical array. A variety of “negative” images were also captured and processed. The ANN were developed using these images and a rigorous training and testing protocol. The Cryptosporidium oocyst ANN were trained with 1,586 images, while Giardia cyst ANN were trained with 2,431 images. After training, the best-performing ANN were selected based on an initial testing performance against 100 images (50 positive and 50 negative images). The networks were validated against previously “unseen” images of 500 Cryptosporidium oocysts (250 positive, 250 negative) and 282 Giardia cysts (232 positive, 50 negative). The selected ANNs correctly identified 91.8 and 99.6% of the Cryptosporidium oocyst and Giardia cyst images, respectively. These results indicate that ANN technology can be an alternate to having trained personnel for detecting these pathogens and can be a boon to underdeveloped regions of the world where there is a chronic shortage of adequately skilled individuals to detect these pathogens.     

A Global Comparison of the Human and T. brucei Degradomes Gives Insights about Possible Parasite Drug Targets

We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups (“M32” and “C51”) that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.     

The leukemia-specific fusion gene ETV6/RUNX1 perturbs distinct key biological functions primarily by gene repression

Background

ETV6/RUNX1 (E/R) (also known as TEL/AML1) is the most frequent gene fusion in childhood acute lymphoblastic leukemia (ALL) and also most likely the crucial factor for disease initiation; its role in leukemia propagation and maintenance, however, remains largely elusive. To address this issue we performed a shRNA-mediated knock-down (KD) of the E/R fusion gene and investigated the ensuing consequences on genome-wide gene expression patterns and deducible regulatory functions in two E/R-positive leukemic cell lines.

Findings

Microarray analyses identified 777 genes whose expression was substantially altered. Although approximately equal proportions were either up- (KD-UP) or down-regulated (KD-DOWN), the effects on biological processes and pathways differed considerably. The E/R KD-UP set was significantly enriched for genes included in the “cell activation”, “immune response”, “apoptosis”, “signal transduction” and “development and differentiation” categories, whereas in the E/R KD-DOWN set only the “PI3K/AKT/mTOR signaling” and “hematopoietic stem cells” categories became evident. Comparable expression signatures obtained from primary E/R-positive ALL samples underline the relevance of these pathways and molecular functions. We also validated six differentially expressed genes representing the categories “stem cell properties”, “B-cell differentiation”, “immune response”, “cell adhesion” and “DNA damage” with RT-qPCR.

Conclusion

Our analyses provide the first preliminary evidence that the continuous expression of the E/R fusion gene interferes with key regulatory functions that shape the biology of this leukemia subtype. E/R may thus indeed constitute the essential driving force for the propagation and maintenance of the leukemic process irrespective of potential consequences of associated secondary changes. Finally, these findings may also provide a valuable source of potentially attractive therapeutic targets.     

Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak

Background

Surveys are popular methods to measure public perceptions in emergencies but can be costly and time consuming. We suggest and evaluate a complementary “infoveillance” approach using Twitter during the 2009 H1N1 pandemic. Our study aimed to: 1) monitor the use of the terms “H1N1” versus “swine flu” over time; 2) conduct a content analysis of “tweets”; and 3) validate Twitter as a real-time content, sentiment, and public attention trend-tracking tool.

Methodology/Principal Findings

Between May 1 and December 31, 2009, we archived over 2 million Twitter posts containing keywords “swine flu,” “swineflu,” and/or “H1N1.” using Infovigil, an infoveillance system. Tweets using “H1N1” increased from 8.8% to 40.5% (R ² = .788; p<.001), indicating a gradual adoption of World Health Organization-recommended terminology. 5,395 tweets were randomly selected from 9 days, 4 weeks apart and coded using a tri-axial coding scheme. To track tweet content and to test the feasibility of automated coding, we created database queries for keywords and correlated these results with manual coding. Content analysis indicated resource-related posts were most commonly shared (52.6%). 4.5% of cases were identified as misinformation. News websites were the most popular sources (23.2%), while government and health agencies were linked only 1.5% of the time. 7/10 automated queries correlated with manual coding. Several Twitter activity peaks coincided with major news stories. Our results correlated well with H1N1 incidence data.

Conclusions

This study illustrates the potential of using social media to conduct “infodemiology” studies for public health. 2009 H1N1-related tweets were primarily used to disseminate information from credible sources, but were also a source of opinions and experiences. Tweets can be used for real-time content analysis and knowledge translation research, allowing health authorities to respond to public concerns.     

Dynamic effective connectivity of inter-areal brain circuits

Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence”, making thus possible a fast “on-demand” reconfiguration of global information routing modalities.