Decoding of human identity by computer vision and neuronal vision

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes. This is the same challenge faced by the nervous system and partially addressed by the concept cells—neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL) ⁠. Yet, access to neurons representing a particular concept is limited due to these neurons’ sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series “24”. First, we devised a minimally supervised CV algorithm (with comparable performance against manually-labeled data) to detect the most prevalent characters (above 1% overall appearance) in each frame. Next, we implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the four main characters throughout the episode. This methodology allowed us to compare “computer vision” with “neuronal vision”—footprints associated with each character present in the activity of a subset of neurons—and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants’ subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL

Nanomaterial-based approaches for prevention of biofilm-associated infections on medical devices and implants

Biofilm formation is a major problem in medical device-related infections leading to failure of implantbased therapies. Though various conventional approaches to counter biofilm formation like physical and/or mechanical removal, chemical removal, and the use of antimicrobials exist, they fail due to increased resistance of biofilms. This review discusses various nanomaterial-based approaches such as the use of metallic and metal oxide nanoparticles- and polymer-based nanocomposites, which are currently being developed for prevention and treatment of biofilms. Nanoparticles of transition metals and their oxides are toxic to microorganisms and exhibit their toxicity through the generation of reactive oxygen species at concentrations that are non-toxic to eukaryotic cells. Other approaches include the entrapment of bioactive agents in polymer/ceramic nanoparticles, for enhanced anti-biofilm activity due to the synergistic effect between them. These nanomaterial-based approaches could play an important role in control and eradication of biofilm related infections and complications associated with medical devices and implants

Self-Activated Fluorescent Hydroxyapatite Nanoparticles: A Promising Agent for Bioimaging and Biolabeling

Bioimaging has drastically transformed the field of medicine, and made the process of diagnosis easy and fast. Visualization of complete organ to complex biological processes has now become possible. Among the various imaging processes, fluorescence imaging using nontoxic fluorescent nanomaterials is advantageous for several beneficial features including high sensitivity, minimal invasiveness, and safe detection limit. In this study, we have synthesized and characterized a new class of nontoxic, self-activated fluorescent hydroxyapatite nanoparticles (fHAps) with different aspect ratios (thin-rods, short-rods, rods) by changing the stabilizing agents (triethyl amine and acetyl acetone) and solvents (water and dimethyl sulfoxide). fHAps showed excellent fluorescence with a broad emission spectrum ranging from 350 to 750 nm and maximum at 502 nm. The presence of fluorescence was attributed to the electronic transition in the asymmetric structure of fHAps as confirmed by ESR spectroscopy and the absence of fluorescence in symmetric HAp NPs. In addition to exceptional fluorescence behavior, these NPs were found to be nontoxic in nature and could be easily internalized in both prokaryotic and eukaryotic systems. We propose that the fHAps provide a safe and a potential alternative to the current fluorescent materials in use for biolabeling and bioimaging applications