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Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

The purpose of this paper is to propose two operators for diversity and mutation control in artificial immune systems (AISs).

The proposed operators are applied in substitution to the suppression and mutation operators used in AISs. The proposed mechanisms were tested in the opt-aiNet, a continuous optimization algorithm inspired in the theories of immunology. The traditional opt-aiNet uses a suppression operator based on the immune network principles to remove similar cells and add random ones to control the diversity of the population. This procedure is computationally expensive, as the Euclidean distances between every possible pair of candidate solutions must be computed. This work proposes a self-organizing suppression mechanism inspired by the self-organizing criticality (SOC) phenomenon, which is less dependent on parameter selection. This work also proposes the use of the q-Gaussian mutation, which allows controlling the form of the mutation distribution during the optimization process. The algorithms were tested in a well-known benchmark for continuous optimization and in a bioinformatics problem: the rigid docking of proteins.

The proposed suppression operator presented some limitations in unimodal functions, but some interesting results were found in some highly multimodal functions. The proposed q-Gaussian mutation presented good performance in most of the test cases of the benchmark, and also in the docking problem.

First, the self-organizing suppression operator was able to reduce the complexity of the suppression stage in the opt-aiNet. Second, the use of q-Gaussian mutation in AISs presented better compromise between exploitation and exploration of the search space and, as a consequence, a better performance when compared to the traditional Gaussian mutation.

This chapter will explore how assessment might look in next generation learning spaces where we have the potential to merge physical and virtual activities. Students now have ready access to a world of resources within their classroom and this fundamentally changes the nature of learning and assessment. The trend toward gamification of learning and assessment will be examined and the issue of assessment in new educational environments such as MOOCs will be explored. The impact of the semantic web (Web 3.0), where web objects and their context are all linked and objects have memory of how an individual student used them on previous occasions, will be discussed.

Next generation learning spaces encapsulate the affordances of both physical and virtual spaces and yet many assessment tasks are still designed as if students occupied only one of these spaces. Teachers will need to design more authentic, meaningful tasks that will engage students in using the full range of their capabilities and available resources, both physical and virtual. Students come together physically to engage in the social construction of their knowledge and can use the virtual spaces to broaden the social dimension of their learning environment.

Gamification of learning and assessment will require new approaches to defining tasks as teachers will need to decide how to incorporate diagnostic, formative, and summative assessment components within a more holistic educational environment. Game theory will be blended with learning theory in curriculum design and will result in the redesign of learning and assessment activities that are based on engagement (flow), user needs, and an evidence-centered design approach.

Pasta is popularly consumed in developed and developing countries. Many low-income homes that could not afford protein-rich foods consumed pasta without further supplements as a meal. However, pasta is deficient in protein and some other health-promoting substances. Deoiled sesame and amaranth flours are rich in protein, minerals, amino acids, antioxidants and many non-nutrient-based health benefits. Formulating a nutrient-dense pasta product (spaghetti) would reduce protein-energy malnutrition (PEM) and improve the health status of pasta consumers. This study aims to investigate some bioactive, nutrients and non-nutrient components of developed and optimized spaghetti pasta from deoiled sesame, amaranth and modified sorghum starch blend.

Amaranth, sorghum and sesame grains were sorted, wet-cleaned and dried. Sesame grain was roasted while starch was extracted from sorghum grain. The innate starch was modified by heat-moisture treatment. The prepared grains and starch were milled into flours and formulated into different flour mixtures using the Box-Behnken design of response surface methodology and the process was modeled and optimized. The flour mixtures were made into spaghetti pasta. Proximate, mineral, amino acids, biological value, protein efficiency ration, phytochemical, antioxidant activity, physico-functional and sensory properties of the formulated spaghetti were evaluated using standard procedures.

An optimal mixture of deoiled sesame (31.12g/100g), amaranth (56.56g/100g) and modified sorghum starch (12.32g/100g) were established, which yielded protein (25.79g/100g), appearance (96.65%), taste (94.57%) and acceptability index (97.37%). The spaghetti was significantly (p-values ranged from 0.001 to 0.018) superior in protein, ash, fat, fiber, calcium, magnesium, zinc, alkaloids, total phenolic, flavonoids, 2,2-diphenyl-1-picryl-hydrazl (DPPH) and ferric ion reducing antioxidant power (FRAP) relative to the control (100% wheat flour). Amino acid showed that the product is rich in histidine, isoleucine, phenylalanine and threonine. The high essential amino acid index of the product indicated that it is a good protein source. The spaghetti was significantly (p-values ranged from 0.001 to 0.021) superior in aroma, taste and acceptability index relative to the control.

This study showed the feasibility of developing spaghetti pasta from deoiled sesame, amaranth grains and modified sorghum starch. The production process described in this study is scalable; and the process could be applied on a small scale for the development of self-entrepreneurs and industrially. The high protein content of the product indicated that it could be used to reduce PEM in developing countries.

The use of sequence and structure databanks is examined in relation to their application in some of the main branches of protein studies. Also the question of availability is addressed by means of presenting some information on current sequence and structure databanks. Increasingly research in molecular science requires joint access to both sequence and structure databases, and the reasons for this development, together with some of the methods for integrated access, are analysed.

Edible insects are increasingly recognized as great food resources of the future. Entomophagy, the practice of eating insects, has a predicted market by 2030 of nearly $8 billion. But this market could grow even more with the acceptance of insects as food by Western consumers, as an alternative to conventional protein sources. In this context, the authors aim to collect and analyze information about consumers' perception of insects as food in Brazil, through the word association task.

In total, 751 participants agreed to complete the word association task in relation to entomophagy through an online questionnaire. They had to write down the first five words or terms that came to their minds when they thought of eating insects.

Individual mentions were categorized according to their similarities and/or proximity. Based on this grouping the authors identified six dimensions, with emphasis on “hedonic attitudes and feelings”, “cultural aspects” and “exotic”. The study concludes that Brazilians see insect consumption as belonging to another culture, not Western.

Few studies have explored the association of words in relation to entomophagy, especially in Brazil. This article aims to fill this gap, using this methodology applied to the Brazilian public.

Natural selection—survival of the fittest—is as old as life itself. Applied genetics which is purposeful in contrast to natural selection also has a long history, particularly in agriculture; it has received impetus from the more exacting demands of the food industry for animal breeds with higher lean : fat and meat : bone ratios, for crops resistant to the teeming world of parasites. Capturing the exquisite scent, the colours and form beautiful of a rose is in effect applied genetics and it has even been applied to man. For example, Frederick the Great, Emperor of Prussia, to maintain a supply of very tall men for his guards—his Prussian Guards averaged seven feet in height—ordered them to marry very tall women to produce offspring carrying the genes of great height. In recent times, however, research and experiment in genetic control, more in the nature of active interference with genetic composition, has developed sufficiently to begin yielding results. It is self‐evident that in the field of micro‐organisms, active interference or manipulations will produce greater knowledge and understanding of the gene actions than in any other field or by any other techniques. The phenomenon of “transferred drug resistance”, the multi‐factorial resistance, of a chemical nature, transferred from one species of micro‐organisms to another, from animal to human pathogens, its role in mainly intestinal pathology and the serious hazards which have arisen from it; all this has led to an intensive study of plasmids and their mode of transmission. The work of the Agricultural Research Council's biologists (reported elsewhere in this issue) in relation to nitrogen‐fixing genes and transfer from one organism able to fix nitrogen to another not previously having this ability, illustrates the extreme importance of this new field. Disease susceptibility, the inhibition of invasiveness which can be acquired by relatively “silent” micro‐organisms, a better understanding of virulence and the possible “disarming” of organisms, particularly those of particular virulence to vulnerable groups. Perhaps this is looking for too much too soon, but Escherichia coli would seem to offer more scope for genetic experiments than most; it has serotypes of much variability and viability; and its life and labours in the human intestine have assumed considerable importance in recent years. The virulence of a few of its serotypes constitute an important field in food epidemiology. Their capacity to transfer plasmids—anent transfer of drug resistance— to strains of other organisms resident in the intestines, emphasizes the need for close study, with safeguards.

The purpose of this paper is to investigate how vegetable side components (carrot and broccoli) influence children’s associations and expected liking of two common Norwegian dishes (meatballs and salmon).

Children aged 11-12 years old were recruited to complete an adapted free association test (n=89). Each participant was exposed to four pictures of dishes that included combinations of the experimental food items served with potatoes and sauce. Participants stated their immediate associations and how much they liked each meal on a five-point smiley face scale for children. Evoked associations were coded and categorized before the relationship between the expected liking of the dishes and the associations were visualized based on correspondence analysis.

The authors found that the expected liking of the dishes was significantly affected by the composition of the dish, that the associations related to the meal changed when the vegetable component changed, and that children’s associations to food are quite similar to what previous studies have found for adults.

How children categorize complex dishes and how their mental representation explains expected liking has rarely been investigated before. By investigating how dishes with different components activate different associations and how these associations co-occur with children’s expected liking of the dishes, the authors gain knowledge that can help us develop strategies for improved dietary change.