A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents.

Spontaneous synaptic activity is a hallmark of biological neural networks. A thorough description of these synaptic signals is essential for understanding neurotransmitter release and the generation of a postsynaptic response. However, the complexity of synaptic current trajectories has either precluded an in-depth analysis or it has forced human observers to resort to manual or semi-automated approaches based on subjective amplitude and area threshold settings. Both procedures are time-consuming, error-prone and likely affected by human bias. Here, we present three complimentary methods for a fully automated analysis of spontaneous excitatory postsynaptic currents measured in major cell types of the mouse retina and in a primary culture of mouse auditory cortex. Two approaches rely on classical threshold methods, while the third represents a novel machine learning-based algorithm. Comparison with frequently used existing methods demonstrates the suitability of our algorithms for an unbiased and efficient analysis of synaptic signals in the central nervous system.

Determinants of Children's Fruit Intake in Teso South Sub-County, Kenya-A Multi-Phase Mixed Methods Study among Households with Children 0-8 Years of Age.

Fruits are micronutrient-rich sources which are often underrepresented in children's diets. More insights into the determinants of children's fruit consumption are needed to improve nutrition education in Teso South Sub-County, Kenya. A multiphase mixed method study was applied among 48 farm households with children 0-8 years of age. A market survey together with focus group discussions were used to design a formative research approach including qualitative and quantitative data collection methods. The unavailability of fruits and the inability to plant fruit trees in the homesteads were the main challenges to improve fruit consumption behaviour, although a number of different fruit species were available on the market or in households. Perceived shortage of fruits, financial constraints to purchase fruits and taste were important barriers. Fruits as snacks given between meals was perceived as helpful to satisfy children. The mean number of fruit trees in the homesteads was positively associated with fruit consumption. Field trials are needed to test how best fruit trees within home gardens and on farms can be included, acknowledging limited space and constraints of households with young children. This should be combined with nutrition education programs addressing perceptions about the social and nutrient value of fruits for children.

The structure dilemma in biological and artificial neural networks.

Brain research up to date has revealed that structure and function are highly related. Thus, for example, studies have repeatedly shown that the brains of patients suffering from schizophrenia or other diseases have a different connectome compared to healthy people. Apart from stochastic processes, however, an inherent logic describing how neurons connect to each other has not yet been identified. We revisited this structural dilemma by comparing and analyzing artificial and biological-based neural networks. Namely, we used feed-forward and recurrent artificial neural networks as well as networks based on the structure of the micro-connectome of C. elegans and of the human macro-connectome. We trained these diverse networks, which markedly differ in their architecture, initialization and pruning technique, and we found remarkable parallels between biological-based and artificial neural networks, as we were additionally able to show that the dilemma is also present in artificial neural networks. Our findings show that structure contains all the information, but that this structure is not exclusive. Indeed, the same structure was able to solve completely different problems with only minimal adjustments. We particularly put interest on the influence of weights and the neuron offset value, as they show a different adaption behaviour. Our findings open up new questions in the fields of artificial and biological information processing research.

Barley RIC171 interacts with RACB in planta and supports entry of the powdery mildew fungus.

RHO-like GTPases of plants (ROPs, also called RACs) are involved in plant development and interaction with the environment. The barley ROP protein RACB is involved in susceptibility to the fungal pathogen Blumeria graminis f.sp. hordei (Bgh). By screening barley sequence databases for potential protein interactors of plant RHO-like proteins, we identified a ROP-interactive CRIB (CDC42/RAC interactive binding) motif containing protein of 171 amino acids (RIC171). The protein interacted with constitutively activated RACB in a targeted yeast two-hybrid assay. By use of split yellow fluorescing protein fusions, we demonstrated that RIC171 interacts with constitutively activated (CA) RACB-G15V but not with dominant negative RACB-T20N in planta. Transient overexpression of RIC171, similar to overexpression of CA RACB-G15V, rendered epidermal cells more susceptible to penetration by Bgh. In contrast, expression of a 46-amino-acid RIC171-CRIB peptide, which was sufficient to interact with CA RACB-G15V, had a dominant negative effect and reduced susceptibility to Bgh. A red fluorescing DsRED-RIC171 fusion protein colocalized with green fluorescing GFP-RACB-G15V at the cell periphery. Coexpression with CA RACB-G15V but not with RACB-T20N increased peripheral localization of DsRED-RIC171. Additionally, DsRED-RIC171 accumulated at sites of fungal attack, suggesting enhanced ROP activity at sites of attempted fungal penetration.