Quantitative Biology

Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio- Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research.

This study extends the mathematical model of emotion dimensions that we previously proposed (Yanagisawa, et al. 2019, Front Comput Neurosci) to consider perceived complexity as well as novelty, as a source of arousal potential. Berlyne's hedonic function of arousal potential (or the inverse U-shaped curve, the so-called Wundt curve) is assumed. We modeled the arousal potential as information contents to be processed in the brain after sensory stimuli are perceived (or recognized), which we termed sensory surprisal. We mathematically demonstrated that sensory surprisal represents free energy, and it is equivalent to a summation of information gain (or information from novelty) and perceived complexity (or information from complexity), which are the collative variables forming the arousal potential. We demonstrated empirical evidence with visual stimuli (profile shapes of butterfly) supporting the hypothesis that the summation of perceived novelty and complexity shapes the inverse U-shaped beauty function. We discussed the potential of free energy as a mathematical principle explaining emotion initiators.

The input-output behaviour of the Wiener neuronal model subject to alternating input is studied under the assumption that the effect of such an input is to make the drift itself of an alternating type. Firing densities and related statistics are obtained via simulations of the sample-paths of the process in the following three cases: the drift changes occur during random periods characterized by (i) exponential distribution, (ii) Erlang distribution with a preassigned shape parameter, and (iii) deterministic distribution. The obtained results are compared with those holding for the Wiener neuronal model subject to sinusoidal input

Electrical stimulation of residual nerves can be used to provide amputees with intuitive sensory feedback. An important aspect of this artificial sensory feedback is the ability to convey the magnitude of tactile stimuli. Using classical psychophysical methods, we quantified the just-noticeable differences for electrocutaneous stimulation pulse frequency in both intact participants and one transradial amputee. For the transradial amputee, we also quantified the just-noticeable difference of intraneural microstimulation pulse frequency via chronically implanted Utah Slanted Electrode Arrays. We demonstrate that intensity discrimination is similar across conditions: intraneural microstimulation of the residual nerves, electrocutaneous stimulation of the reinnervated skin on the residual limb, and electrocutaneous stimulation of intact hands. We also show that intensity discrimination performance is significantly better at lower pulse frequencies than at higher ones - a finding that's unique to electrocutaneous and intraneural stimulation and suggests that supplemental sensory cues may be present at lower pulse frequencies. These results can help guide the implementation of artificial sensory feedback for sensorized bionic arms.

A significant subset of patients infected by SARS-CoV-2 presents olfactory, taste, and/or chemesthesis (OTC) disorders (OTCD). These patients recover rapidly, eliminating damage of sensory nerves. Discovering that S protein contains two ankyrin repeat binding motifs (S-ARBMs) and some TRP cation channels, implicated in OTC, have ankyrin repeat domains (TRPs-ARDs), I hypothesized that interaction of S-ARBMs and TRPs-ARDs could dysregulate the function of the latter and thus explains OTCD. Of note, some TRPs-ARDs are expressed in the olfactory epithelium, taste buds, trigeminal neurons in the oronasal cavity and vagal neurons in the trachea/lungs. Furthermore, this hypothesis is supported by studies that have shown: (i) respiratory viruses interact with TRPA1 and TRPV1 on sensory nerves and epithelial cells in the airways, (ii) the respiratory pathophysiology in COVID-19 patients is similar to lungs injuries produced by the sensitization of TRPV1 and TRPV4, and (iii) resolvin D1 and D2 shown to reduce SARS-CoV-2-induced inflammation, directly inhibit TRPA1, TRPV1, TRPV3 and TRPV4. Herein, results of blind dockings of S-ARBMs, 408-RQIAPG-413 (in RBD but distal from the ACE-2 binding region) and 905-RFNGIG-910 (in HR1), into TRPA1, TRPV1 and TRPV4 suggest that S-ARBMs interact with ankyrin repeat 6 of TRPA1 near an active site, and ankyrin repeat 3-4 of TRPV1 near cysteine 258 supposed to be implicated in the formation of inter-subunits disulfide bond. These findings suggest that S-ARBMs affect TRPA1, TRPV1 and TRPV4 function by interfering with channel assembly and trafficking. After an experimental confirmation of these interactions, among possible preventive treatments against COVID-19, the use of pharmacological manipulation (probably inhibition) of TRPs-ARDs to control or mitigate sustained pro-inflammatory response.

Multimodal fusion benefits disease diagnosis by providing a more comprehensive perspective. Developing algorithms is challenging due to data heterogeneity and the complex within- and between-modality associations. Deep-network-based data-fusion models have been developed to capture the complex associations and the performance in diagnosis has been improved accordingly. Moving beyond diagnosis prediction, evaluation of disease mechanisms is critically important for biomedical research. Deep-network-based data-fusion models, however, are difficult to interpret, bringing about difficulties for studying biological mechanisms. In this work, we develop an interpretable multimodal fusion model, namely gCAM-CCL, which can perform automated diagnosis and result interpretation simultaneously. The gCAM-CCL model can generate interpretable activation maps, which quantify pixel-level contributions of the input features. This is achieved by combining intermediate feature maps using gradient-based weights. Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis. We validate the gCAM-CCL model on a brain imaging-genetic study, and show gCAM-CCL's performed well for both classification and mechanism analysis. Mechanism analysis suggests that during task-fMRI scans, several object recognition related regions of interests (ROIs) are first activated and then several downstream encoding ROIs get involved. Results also suggest that the higher cognition performing group may have stronger neurotransmission signaling while the lower cognition performing group may have problem in brain/neuron development, resulting from genetic variations.

Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological difference in specific brain regions can be found on MRI with the means of Convolution Neural Networks (CNN). However, interpretation of the existing models is based on a region of interest and can not be extended to voxel-wise image interpretation on a whole image. In the current work, we consider the classification task on a large-scale open-source dataset of young healthy subjects -- an exploration of brain differences between men and women. In this paper, we extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans. We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods: Meaningful Perturbations, Grad CAM and Guided Backpropagation, and contribute with the open-source library.

Since the early work of Bernard Katz, the process of cellular chemical communication via exocytosis, quantal release, has been considered to be all or none. Recent evidence has shown exocytosis to be partial or 'subquantal' at single-cell model systems, but there is a need to understand this at communicating nerve cells. Partial release allows nerve cells to control the signal at the site of release during individual events, where the smaller the fraction released, the greater the range of regulation. Here we show that the fraction of the vesicular octopamine content released from a living Drosophila larval neuromuscular neuron is very small. The percentage of released molecules was found to be only 4.5% for simple events and 10.7% for complex (i.e., oscillating or flickering) events. This large content, combined with partial release controlled by fluctuations of the fusion pore, offers presynaptic plasticity that can be widely regulated. Two works published in 2010 suggested that the Katz principle, [1] was incorrect for all-or-none release and that only part of the chemical load of vesicles was released during exocytosis, at least as measured as a full spike during amperometry. [2] The combination of electrochemical methods to measure both release and vesicle content in 2015 added a wealth of information to support the concept of partial release in exocytosis. [3] Additionally, this has recently been supported by work with TIRF microscopy showing 'subquantal' release from vesicles in adrenal chromaffin cells and using super-resolution STED microscopy. [4] It appears that the full event generally involves release of only part of the load of chemical messenger in single-cell model systems like adrenal chromaffin and PC12 cells. Is this also true at living neurons in a nervous system and to what extent? To answer this critical question, we quantified the number of octopamine molecules in the neuromuscular neurons of Drosophila larvae by adapting an amperometric technique developed in our

Recent technological advances in brain recording and artificial intelligence are propelling a new paradigm in neuroscience beyond the traditional controlled experiment. Rather than focusing on cued, repeated trials, naturalistic neuroscience studies neural processes underlying spontaneous behaviors performed in unconstrained settings. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. Here we describe an automated approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and naturalistic behavior video data. We take a behavior-first approach to analyzing the long-term recordings. Using a combination of computer vision, discrete latent-variable modeling, and string pattern-matching on the behavioral video data, we find and annotate spontaneous human upper-limb movement events. We show results from our approach applied to data collected for 12 human subjects over 7--9 days for each subject. Our pipeline discovers and annotates over 40,000 instances of naturalistic human upper-limb movement events in the behavioral videos. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate prior findings from traditional controlled experiments. We also prototype a decoder for a movement initiation detection task to demonstrate the efficacy of our pipeline as a source of training data for brain-computer interfacing applications. Our work addresses the unique data analysis challenges in studying naturalistic human behaviors, and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publicly release our curated dataset, providing a resource to study naturalistic neural and behavioral variability at a scale not previously available.

We propose a non-substance dualism theory, following Heisenberg: The world consists of both ontologically real possibilities that do not obey Aristotle's Law of the Excluded Middle, and ontologically real Actuals that do o0bey the Law of the Excluded Middle. This quantum approach solves five issues in quantum mechanics and numerous puzzles about the mind-brain relationship. It raises the possibility that some aspects of mind are non-local, and that mind plays an active role in the physical world. We present supporting evidence.