Enhanced hydrolysis of cellulose hydrogels by morphological modification

Bioprocess and Biosystems Engineering - Cellulose is one of the most abundant bio-renewable materials on earth, yet the potential of cellulosic bio-fuels is not fully exploited, primarily due to...     

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.     

The gradient clusteron: A model neuron that learns to solve classification tasks via dendritic nonlinearities,structural plasticity,and gradient descent

Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via nonlinear voltage-dependent mechanisms, such as NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of “under-performing” synapses on a model dendrite during learning (“structural plasticity”), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location-dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the all-versus-all MNIST task (~85%) approaches that of logistic regression (~93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron (“functional plasticity”) and show that a G-clusteron that utilizes the weight update rule can achieve ~89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions.     

Differential Synaptic Input to External Globus Pallidus Neuronal Subpopulations In Vivo

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LL-37-mediated activation of host receptors is critical for defense against group A streptococcal infection

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What can mitochondrial heterogeneity tell us about mitochondrial dynamics and autophagy?

A growing body of evidence shows that mitochondria are heterogeneous in terms of structure and function. Increased heterogeneity has been demonstrated in a number of disease models including ischemia-reperfusion and nutrient-induced beta cell dysfunction and diabetes. Subcellular location and proximity to other organelles, as well as uneven distribution of respiratory components have been considered as the main contributors to the basal level of heterogeneity. Recent studies point to mitochondrial dynamics and autophagy as major regulators of mitochondrial heterogeneity. While mitochondrial fusion mixes the content of the mitochondrial network, fission dissects the mitochondrial network and generates depolarized segments. These depolarized mitochondria are segregated from the networking population, forming a pre-autophagic pool contributing to heterogeneity. The capacity of a network to yield a depolarized daughter mitochondrion by a fission event is fundamental to the generation of heterogeneity. Several studies and data presented here provide a potential explanation, suggesting that protein and membranous structures are unevenly distributed within the individual mitochondrion and that inner membrane components do not mix during a fusion event to the same extent as the matrix components do. In conclusion, mitochondrial subcellular heterogeneity is a reflection of the mitochondrial lifecycle that involves frequent fusion events in which components may be unevenly mixed and followed by fission events generating disparate daughter mitochondria, some of which may fuse again, others will remain solitary and join a pre-autophagic pool.     

Localized Sampling Enables Monitoring of Cell State via Inline Electrospray Ionization Mass Spectrometry

Nascent advanced therapies, including regenerative medicine and cell and gene therapies, rely on the production of cells in bioreactors that are highly heterogeneous in both space and time. Unfortunately, advanced therapies have failed to reach a wide patient population due to unreliable manufacturing processes that result in batch variability and cost prohibitive production. This can be attributed largely to a void in existing process analytical technologies (PATs) capable of characterizing the secreted critical quality attribute (CQA) biomolecules that correlate with the final product quality. The Dynamic Sampling Platform (DSP) is a PAT for cell bioreactor monitoring that can be coupled to a suite of sensor techniques to provide real-time feedback on spatial and temporal CQA content in situ. In this study, DSP is coupled with electrospray ionization mass spectrometry and direct-from-culture sampling to obtain measures of CQA content in bulk media and the cell microenvironment throughout the entire cell culture process (≈3 weeks). Post hoc analysis of this real-time data reveals that sampling from the microenvironment enables cell state monitoring (e.g., confluence, differentiation). These results demonstrate that an effective PAT should incorporate both spatial and temporal resolution to serve as an effective input for feedback control in biomanufacturing.     

Sensory and Behavioral Components of Neocortical Signal Flow in Discrimination Tasks with Short-Term Memory

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Changes in the Growth Hormone-IGF-I Axis in Non-obese Diabetic Mice

We investigated the changes in GH-IGF-I axis in non-obese diabetic (NOD)-mice, a model of insulin dependent diabetes mellitus. Diabetic female NOD mice and their age- and sex-matched controls were sacrificed at 4, 14, 21 and 30 days (30d DM) after the onset of glycosuria. Serum GH levels increased and serum IGF-I levels decreased in the 30d DM group (182 ± 32% and 45 ± 24% of age-matched controls respectively, p < 0.05). Another group (30d DM + I) was given SC insulin, and its serum IGF-I levels remained decreased. Liver GH receptor (GHR) and GH binding protein (GHBP) mRNA levels, as well as liver membrane GH binding assays were deeply decreased in the 30d DM group in comparison to controls. GHR message and binding capacity remained decreased in the 30d DM + I group. Renal GHR mRNA was decreased at 21d DM but not at 14d DM, whereas GHBP mRNA remained unchanged throughout the experiment. In conclusion, increased serum GH levels are documented in NOD diabetic mice, similarly to the changes described in humans. The decrease in GHR levels and decreased serum IGF-I in spite of increased circulating GH suggest a state of GH resistance.     

Estimate of within population incremental selection through branch imbalance in lineage trees

Incremental selection within a population, defined as limited fitness changes following mutation, is an important aspect of many evolutionary processes. Strongly advantageous or deleterious mutations are detected using the synonymous to non-synonymous mutations ratio. However, there are currently no precise methods to estimate incremental selection. We here provide for the first time such a detailed method and show its precision in multiple cases of micro-evolution. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we propose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. The method requires a high enough number of sequences, and a large enough number of independent mutations. It assumes that all mutations are independent events. It does not require of a baseline model and is practically not affected by sampling biases. We show the method''s wide applicability by testing it on multiple cases of micro-evolution. We show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens.