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Poster by Ikhlak Ahmed (Sidra Medicine), Mubarak Ziab (Sidra Medicine), Sahar Da’as (Sidra Medicine, Hamad Bin Khalifa University), Waseem Hasan (Sidra Medicine), Sujitha P. Jeya (Sidra Medicine), Elbay Aliyev (Sidra Medicine), Sabah Nisar (Sidra Medicine), Ajaz A. Bhat (Sidra Medicine), Khalid Fakhro (Sidra Medicine, Hamad Bin Khalifa University, Weill Cornell Medicine College - Qatar), Ammira S. Alshabeeb Akil (Sidra Medicine) Background: Diabetic nephropathy (DN) is one of the most established microvascular complications of diabetes and a key cause of end-stage renal disease. It is well established that gene susceptibility to DN plays a critical role in disease pathophysiology. Therefore, many genetic studies have been performed to categorize candidate genes in prominent diabetic cohorts, aiming to investigate DN pathogenesis and etiology. Objective: The objective of this study is to identify critical transcriptional factors associated with diabetic nephropathy (DN) progression and validate their role in DN development using a hyperglycemic zebrafish model. Methods: In this study, we performed a meta-analysis on the expression profiles of GSE1009, GSE30122, GSE96804, GSE99340, GSE104948, GSE104954, and GSE111154 to identify critical transcriptional factors associated with DN progression. The analysis was conducted for all individual datasets for each kidney tissue (glomerulus, tubules, and kidney cortex). Results: We identified distinct clusters of susceptibility genes that were dysregulated in a renal compartment- specific pattern. Further, we recognized a small but a closely connected set of these susceptibility genes enriched for podocyte differentiation, several of which were characterized as genes encoding critical transcriptional factors (TFs) involved in DN development and podocyte function. To validate the role of identified TFs in DN progression, we functionally validated the three main TFs (DACH1, LMX1B, and WT1) identified through differential gene expression and network analysis using the hyperglycemic zebrafish model. We report that hyperglycemia-induced altered gene expression of the key TF genes leads to morphological abnormalities in zebrafish glomeruli, pronephric tubules, proximal and distal ducts. Conclusion: This study demonstrated that altered expression of these TF genes could be associated with hyperglycemia-induced nephropathy and, thus, aids in understanding the molecular drivers, essential genes, and pathways that trigger DN initiation and development.

Additional file 1.

Cross-situational word learning (CSWL) is the ability to overcome ambiguity in individually ambiguous scenarios by tracking word-object co-occurrence statistics over multiple such naming scenarios to learn correct word-object mappings. Force choice tests of adults in lab experiments on CSWL has demonstrated adults’ ability to learn words under referential uncertainty (Trueswell, Medina, Hafri, & Gleitman, 2013). Furthermore, eye-tracking data in these and other studies (Yu, Zhong, & Fricker, 2012) demonstrate that participants who are better at learning such words spend more time looking at the target object when named, indicating that eye-movements are a fair reflection of learning accuracy in such tasks. These looking behaviours may, however, be influenced by the perceived characteristics of the object, prior knowledge of the word referent pairs, familiarity with words, and the competition due to other distractors (Yu et al., 2012). In particular, while there is strong evidence to suggest that object properties (like colour and shape) directly affect object categorisation and learning their names (Jiang & Chun, 2001; Landau, Smith, & Jones, 1988; Smith, Jones, Landau, Gershkoff-Stowe, & Samuelson, 2002), the effect has never been demonstrated in cross-situational word learning with systemic property variations. Therefore, in this experiment we plan to use object stimuli of systematic variations in colour and shape properties in a cross-situational learning task and employ eye-tracking to understand the effect of these metric variations on real-time attention and word learning. To assess the metric effects, we will control the difference between the object properties on each training trial to create two conditions: The ‘Near’ condition where the two objects on each learning trial are very similar either in shape or in colour. The ‘’Far condition where the objects presented within a learning trial are very different in shape and colour. Critically, the same objects are used in both conditions: it’s just the pairing that creates metric conflict in the moment. Importantly, in the ‘Near’ condition, there is always similarity in one of the feature dimensions.

The most striking feature of any cognitive system is its ability to keep learning cumulatively and exploit its experiences prospectively to anticipate, reason and produce goal-oriented behaviors. At the core of this intriguing capability lies our memory. Every touch, sound, sight, taste triggers our memory, enabling us to remember and flexibly connect our past experiences, with the available present to the possible future. While emerging trends in neurosciences are rapidly enriching our understanding of the functional organization of memory in the brain, a prospective memory architecture is also a central design feature necessary if robots are to truly become commonplace assistants in numerous application domains (domestic, industrial, others) and in the complexity of the environments we inhabit and create (where neither everything can be known nor can everything be experienced). Hence, with the central premise that cognition is constructive manipulation of memory, this thesis proposes a novel brain guided perspective on the design of cognitive architectures for cumulatively developing systems. Set up in the context of several experiments from animal and infant cognition reenacted on the iCub humanoid, a principled framework for cumulative learning of actions, skills, affordances and cause-effect relations through multiple streams (imitation, exploration, linguistic inputs) is proposed. How such diverse experiences of the robot learnt in past are recalled based on present context, combined with explorative actions to learn something new, creatively connected to generate novel behaviors in the context of sought goals is demonstrated through several tasks. The proposed integrated machinery provides for a shared computational basis for recalling of the past and simulation of the future in line with the emerging trends from neuroscience (like the brain’s default mode network). A notable transition further is the porting of the framework developed on iCub humanoid to real world industrial settings in tasks like assembly and manufacturing (facilitating runtime reasoning and quick switchover to novel assembly tasks). In this sense, the proposed architecture brings together the fields of Robotics, Developmental Psychology, and Neuroscience to craft cognitive and adaptable End User Applications while at the same time provides insights into the functional organization of the brain. In parallel, it lays the foundation for a new generation of cognitive architectures that are domain agnostic (i.e. support open-ended learning), partially embodiment agnostic (i.e. are configurable to new robotic platforms), partially self-driven (i.e. can generate their own internal goals) and brain guided.

The most striking feature of any cognitive system is its ability to keep learning cumulatively and exploit its experiences prospectively to anticipate, reason and produce goal-oriented behaviors. At the core of this intriguing capability lies our memory. Every touch, sound, sight, taste triggers our memory, enabling us to remember and flexibly connect our past experiences, with the available present to the possible future. While emerging trends in neurosciences are rapidly enriching our understanding of the functional organization of memory in the brain, a prospective memory architecture is also a central design feature necessary if robots are to truly become commonplace assistants in numerous application domains (domestic, industrial, others) and in the complexity of the environments we inhabit and create (where neither everything can be known nor can everything be experienced). Hence, with the central premise that cognition is constructive manipulation of memory, this thesis proposes a novel brain guided perspective on the design of cognitive architectures for cumulatively developing systems. Set up in the context of several experiments from animal and infant cognition reenacted on the iCub humanoid, a principled framework for cumulative learning of actions, skills, affordances and cause-effect relations through multiple streams (imitation, exploration, linguistic inputs) is proposed. How such diverse experiences of the robot learnt in past are recalled based on present context, combined with explorative actions to learn something new, creatively connected to generate novel behaviors in the context of sought goals is demonstrated through several tasks. The proposed integrated machinery provides for a shared computational basis for recalling of the past and simulation of the future in line with the emerging trends from neuroscience (like the brain’s default mode network). A notable transition further is the porting of the framework developed on iCub humanoid to real world industrial settings in tasks like assembly and manufacturing (facilitating runtime reasoning and quick switchover to novel assembly tasks). In this sense, the proposed architecture brings together the fields of Robotics, Developmental Psychology, and Neuroscience to craft cognitive and adaptable End User Applications while at the same time provides insights into the functional organization of the brain. In parallel, it lays the foundation for a new generation of cognitive architectures that are domain agnostic (i.e. support open-ended learning), partially embodiment agnostic (i.e. are configurable to new robotic platforms), partially self-driven (i.e. can generate their own internal goals) and brain guided.