An investigation into spike-based neuromorphic approaches for artificial olfactory systems

The implementation of neuromorphic methods has delivered promising results for vision and auditory sensors. These methods focus on mimicking the neuro-biological architecture to generate and process spike-based information with minimal power consumption. With increasing interest in developing low-power and robust chemical sensors, the application of neuromorphic engineering concepts for electronic noses has provided an impetus for research focusing on improving these instruments. While conventional e-noses apply computationally expensive and power-consuming data-processing strategies, neuromorphic olfactory sensors implement the biological olfaction principles found in humans and insects to simplify the handling of multivariate sensory data by generating and processing spike-based information. Over the last decade, research on neuromorphic olfaction has established the capability of these sensors to tackle problems that plague the current e-nose implementations such as drift, response time, portability, power consumption and size. This article brings together the key contributions in neuromorphic olfaction and identifies future research directions to develop near-real-time olfactory sensors that can be implemented for a range of applications such as biosecurity and environmental monitoring. Furthermore, we aim to expose the computational parallels between neuromorphic olfaction and gustation for future research focusing on the correlation of these senses

Electronic Nose as an NDT Tool for Aerospace Industry

AbstractArtificial olfaction is an emerging technology aiming to develop tools for easy, rapid and mobile gas mixture analysis. So far, its application to several application fields is under investigation with some commercial solution already deployed. In this work we present the results of the development process for an electronic nose devised for NDT in aerospace industry focusing on its pattern recognition stage

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis

One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager, is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities

Immunomodulatory biomimetic nanoparticles target articular cartilage trauma after systemic administration

Post-traumatic osteoarthritis (PTOA) is one of the leading causes of disability in developed countries and accounts for 12% of all osteoarthritis cases in the United States. After trauma, inflammatory cells (macrophages amongst others) are quickly recruited within the inflamed synovium and infiltrate the joint space, initiating dysregulation of cartilage tissue homeostasis. Current therapeutic strategies are ineffective, and PTOA remains an open clinical challenge. Here, the targeting potential of liposome-based nanoparticles (NPs) is evaluated in a PTOA mouse model, during the acute phase of inflammation, in both sexes. NPs are composed of biomimetic phospholipids or functionalized with macrophage membrane proteins. Intravenous administra-tion of NPs in the acute phase of PTOA and advanced in vivo imaging techniques reveal prefer-ential accumulation of NPs within the injured joint for up to 7 days post injury, in comparison to controls. Finally, imaging mass cytometry uncovers an extraordinary immunomodulatory effect of NPs that are capable of decreasing the amount of immune cells infiltrating the joint and conditioning their phenotype. Thus, biomimetic NPs could be a powerful theranostic tool for PTOA as their accumulation in injury sites allows their identification and they have an intrinsic immunomodulatory effect

Memcapacitive Devices in Logic and Crossbar Applications

Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits, reducing the energy consumption is limited by the resistive nature of the devices. Memcapacitors would address that limitation while still having all the benefits of memristors. Recent work has shown that with adjusted parameters during the fabrication process, a metal-oxide device can indeed exhibit a memcapacitive behavior. We introduce novel memcapacitive logic gates and memcapacitive crossbar classifiers as a proof of concept that such applications can outperform memristor-based architectures. The results illustrate that, compared to memristive logic gates, our memcapacitive gates consume about 7x less power. The memcapacitive crossbar classifier achieves similar classification performance but reduces the power consumption by a factor of about 1,500x for the MNIST dataset and a factor of about 1,000x for the CIFAR-10 dataset compared to a memristive crossbar. Our simulation results demonstrate that memcapacitive devices have great potential for both Boolean logic and analog low-power applications

Combining Two Selection Principles: Sensor Arrays Based on Both Biomimetic Recognition and Chemometrics

Electronic noses mimic smell and taste senses by using sensor arrays to assess complex samples and to simultaneously detect multiple analytes. In most cases, the sensors forming such arrays are not highly selective. Selectivity is attained by pattern recognition/chemometric data treatment of the response pattern. However, especially when aiming at quantifying analytes rather than qualitatively detecting them, it makes sense to implement chemical recognition via receptor layers, leading to increased selectivity of individual sensors. This review focuses on existing sensor arrays developed based on biomimetic approaches to maximize chemical selectivity. Such sensor arrays for instance use molecularly imprint polymers (MIPs) in both e-noses and e-tongues, for example, to characterize headspace gas compositions or to detect protein profiles. Other array types employ entire cells, proteins, and peptides, as well as aptamers, respectively, in multisensor systems. There are two main reasons for combining chemoselectivity and chemometrics: First, this combined approach increases the analytical quality of quantitative data. Second, the approach helps in gaining a deeper understanding of the olfactory processes in nature

Metal-Organic Frameworks as Bacteria Mimicking Delivery Systems for Tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb), with an estimated 1.5 million deaths and 10 million infections each year. Although TB can be effectively treated with antibiotics, because of the complications and length of treatment, many people fail to complete the treatment which exacerbates the emergence of drug-resistant M. tb strains. The goal of this work is to develop biomimetic particles as host-directed therapy to target the infected macrophages. Two types of metal-organic frameworks (MOFs), MIL-100(Fe) and MIL-88A(Fe) were developed for bacteriamimicking particles. As a proof-of-concept, Mannose was selected as a ligand to target macrophages because many pathogens express mannose on the surface. MOFs were successfully modified with mannose via EDC/NHS coupling method. No difference was observed in cell uptake between MIL-100(Fe) and mannose-MIL-100(Fe). Mannose- MIL-88A(Fe) showed a significant increase in macrophage uptake compared to its unmodified counterpart. MIL-88A(Fe) is rod-shaped and has a size similar to M. tb making it a natural platform for mycobacteria mimicking. MIL-88A(Fe) was coated with two-layer hybrid lipids and mycolic acid (MA), the most abundant lipid in mycobacteria cell wall, was also incorporated. The coating was confirmed by transmission electron microscopy with energy dispersive x-ray analysis (TEM-EDX). Lipids coated MIL- 88A(Fe) with MA directly extracted from Mycobacterium. Avium exhibited the highest cell uptake compared to lipids coated MIL-88A(Fe) with commercial MA or without MA. As MIL-100(Fe) is readily taken by macrophages, unlike MIL-88A(Fe) for bacteria mimicking, MIL-100 (Fe) nanoparticles were designed to have immunomodulatory property by functionalized with the immunomodulatory ligand curdlan. Curdlan coated MIL-100(Fe) was prepared by nanoprecipitation method. The difference in surface charge, intracellular stability, and thermal property confirms the coat of curdlan on MIL- 100(Fe). Overall, MOFs are promising candidates for the development of biomimetic particles as HDT to target infected cells. M.tb-mimetic MIL-88A(Fe) particles and immunomodulator MIL-100(Fe) may potentially enhance host cell response to an M.tb infection by encapsulated HDT drugs or the carriers themselves