Dynamic signaling on branching axons is critical for rapid and efficient communication between neurons in the brain. Efficient signaling in axon arbors depends on a trade-off between the time it takes action potentials to reach synaptic terminals (temporal cost) and the amount of cellular material associated with the wiring path length of the neuron’s morphology (material cost). However, where the balance between structural and dynamical considerations for achieving signaling efficiency is, and the design principle that neurons optimize to preserve this balance, is still elusive. In this work, we introduce a novel analysis that compares morphology and signaling dynamics in axonal networks to address this open problem. We show that in Basket cell neurons the design principle being optimized is the ratio between the refractory period of the membrane, and action potential latencies between the initial segment and the synaptic terminals. Our results suggest that the convoluted paths taken by axons reflect a design compensation by the neuron to slow down signaling latencies in order to optimize this ratio. Deviations in this ratio may result in a breakdown of signaling efficiency in the cell. These results pave the way to new approaches for investigating more complex neurophysiological phenomena that involve considerations of neuronal structure-function relationships.
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.
Understanding how the structural connectivity of a network constrains the dynamics it is able to support is a very active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way, independent of the biophysical or molecular details of the cells themselves. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motor neuron activations in ventral (VB) and dorsal (DB) classes of motor neurons emerged from the simulations, which are qualitatively similar to rhythmic motor neuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent - and we propose - purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.
Background: Psoriasis being an autoimmune disease, affecting almost 3% of the world's population has been an agonizing problem both to the patient and to the doctor who treat them. It has been confusing to the world since biblical times as to it origin and course. Though not contagious, contrary to the beliefs of our fore fathers, the disease is still associated with social stigma and thus has a negative psychological effect on the patient. Though not completely curable, the modern medicine can help in bringing down the severity of the disease if diagnosed correctly and thus uplift the sufferers. Methodology: This study was conducted on 75 patients who attended the OPD of the Department of Dermatology, Sree Gokulam Medical College and Research Foundation, with an aim to decrease the dilemma of diagnosing psoriasis and differentiating it from psoriasiform dermatitis for a period of one year. Results:The histopathological parameters were compared among the 53 patients who were divided into two groups, psoriasis and psoriasiform dermatitis. The different histopathological parameters were compared among these two groups. Of these parameters, parakeratosis, hypogranulosis, acanthosis, regular elongation of rete ridges, mitosis extending beyond the basal layer of epidermis, pallor in the upper layers of epidermis, suprapapillary thinning, dermal oedema and dilated and tortuous blood vessels in the papillary dermis have been evaluated as significant determinants to the diagnosis of psoriasis even in the absence of Munro microabscess and spongiform pustule of Kogoj. Conclusion:The study proved helpful in assessing the determinants that would favor the diagnosis of psoriasis in the absence of a classical histopathological picture, thus aiding in the treatment of the patient.
Background: Neonatal septicemia is defined as a clinical syndrome characterized by systemic signs and symptoms caused by a bacterial infection and gives positive blood culture in the first month of life. It is associated with high morbidity and mortality, but early diagnosis and treatment significantly improve the outcomes. The present study brings out a quick and cost- effective Hematological Scoring System that enables early diagnosis of neonatal sepsis Method: This study was conducted in the Department of Pathology of Sree Gokulam Medical College and Research Foundation, Venjaramoodu, Thiruvananthapuram over 1 year. Eighty neonates with clinical suspicion of sepsis were studied with respect to their peripheral smear findings, blood culture and C-reactive protein levels. Result: Among the eighty cases, twelve neonates were culture positive. Male gender and late- onset of sepsis were the significant risk parameters. Escherichia coli and Staphylococcus aureus were the most common isolated organisms. Of the different parameters studied I:T ratio and Absolute neutrophil count showed the highest specificity and immature neutrophil count had the highest sensitivity. Forty-one percent of neonates had a high Hematological Scoring System score. The specificity of Hematological Scoring System with a score > 5 was 92% Conclusion: Hematological Scoring System is a cost-effective, rapid and easy to perform screening test that can be used to rule out sepsis thus avoiding unnecessary administration of antibiotics to unaffected babies. It should be adopted as a routine screening procedure by minimally qualified rural doctors with minimal resources to ensure appropriate action immediately for children with high index of suspicion of sepsis.
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