An IoT-Enabled E-Nose for Remote Detection and Monitoring of Airborne Pollution Hazards Using LoRa Network Protocol.

Detection and monitoring of airborne hazards using e-noses has been lifesaving and prevented accidents in real-world scenarios. E-noses generate unique signature patterns for various volatile organic compounds (VOCs) and, by leveraging artificial intelligence, detect the presence of various VOCs, gases, and smokes onsite. Widespread monitoring of airborne hazards across many remote locations is possible by creating a network of gas sensors using Internet connectivity, which consumes significant power. Long-range (LoRa)-based wireless networks do not require Internet connectivity while operating independently. Therefore, we propose a networked intelligent gas sensor system (N-IGSS) which uses a LoRa low-power wide-area networking protocol for real-time airborne pollution hazard detection and monitoring. We developed a gas sensor node by using an array of seven cross-selective tin-oxide-based metal-oxide semiconductor (MOX) gas sensor elements interfaced with a low-power microcontroller and a LoRa module. Experimentally, we exposed the sensor node to six classes i.e., five VOCs plus ambient air and as released by burning samples of tobacco, paints, carpets, alcohol, and incense sticks. Using the proposed two-stage analysis space transformation approach, the captured dataset was first preprocessed using the standardized linear discriminant analysis (SLDA) method. Four different classifiers, namely AdaBoost, XGBoost, Random Forest (RF), and Multi-Layer Perceptron (MLP), were then trained and tested in the SLDA transformation space. The proposed N-IGSS achieved "all correct" identification of 30 unknown test samples with a low mean squared error (MSE) of 1.42 × 10-4 over a distance of 590 m.

A Smart Card-Based Two-Factor Mutual Authentication Scheme for Efficient Deployment of an IoT-Based Telecare Medical Information System.

The integration of the Internet of Things (IoT) and the telecare medical information system (TMIS) enables patients to receive timely and convenient healthcare services regardless of their location or time zone. Since the Internet serves as the key hub for connection and data sharing, its open nature presents security and privacy concerns and should be considered when integrating this technology into the current global healthcare system. Cybercriminals target the TMIS because it holds a lot of sensitive patient data, including medical records, personal information, and financial information. As a result, when developing a trustworthy TMIS, strict security procedures are required to deal with these concerns. Several researchers have proposed smart card-based mutual authentication methods to prevent such security attacks, indicating that this will be the preferred method for TMIS security with the IoT. In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC's finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. The results of a security analysis indicate that the newly contributed scheme is resistant to a wide variety of cryptographic attacks. A comparison of computation and communication costs demonstrates that the proposed scheme is more cost-effective than existing schemes.

A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends.

Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system's capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.

NOMA-Based VLC Systems: A Comprehensive Review.

The enhanced proliferation of connected entities needs a deployment of innovative technologies for the next generation wireless networks. One of the critical concerns, however, is the spectrum scarcity, due to the unprecedented broadcast penetration rate nowadays. Based on this, visible light communication (VLC) has recently emerged as a viable solution to secure high-speed communications. VLC, a high data rate communication technology, has proven its stature as a promising complementary to its radio frequency (RF) counterpart. VLC is a cost-effective, energy-efficient, and secure technology that exploits the current infrastructure, specifically within indoor and underwater environments. Yet, despite their appealing capabilities, VLC systems face several limitations which constraint their potentials such as LED's limited bandwidth, dimming, flickering, line-of-sight (LOS) requirement, impact of harsh weather conditions, noise, interference, shadowing, transceiver alignment, signal decoding complexity, and mobility issue. Consequently, non-orthogonal multiple access (NOMA) has been considered an effective technique to circumvent these shortcomings. The NOMA scheme has emerged as a revolutionary paradigm to address the shortcomings of VLC systems. The potentials of NOMA are to increase the number of users, system's capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. Motivated by this, the presented study offers an overview of NOMA-based VLC systems. This article provides a broad scope of existing research activities of NOMA-based VLC systems. This article aims to provide firsthand knowledge of the prominence of NOMA and VLC and surveys several NOMA-enabled VLC systems. We briefly highlight the potential and capabilities of NOMA-based VLC systems. In addition, we outline the integration of such systems with several emerging technologies such as intelligent reflecting surfaces (IRS), orthogonal frequency division multiplexing (OFDM), multiple-input and multiple-output (MIMO) and unmanned aerial vehicles (UAVs). Furthermore, we focus on NOMA-based hybrid RF/VLC networks and discuss the role of machine learning (ML) tools and physical layer security (PLS) in this domain. In addition, this study also highlights diverse and significant technical hindrances prevailing in NOMA-based VLC systems. We highlight future research directions, along with provided insights that are envisioned to be helpful towards the effective practical deployment of such systems. In a nutshell, this review highlights the existing and ongoing research activities for NOMA-based VLC systems, which will provide sufficient guidelines for research communities working in this domain and it will pave the way for successful deployment of these systems.

Caught red handed: modeling and confirmation of the myeloperoxidase ceruloplasmin alpha-thrombin complex.

The work is devoted to the study of the structural characteristics of the myeloperoxidase-ceruloplasmin-thrombin complex using small-angle neutron scattering methods in combination with computer modeling, as well as surface plasmon resonance and solid-phase enzyme assay. We have previously shown that the functioning of active myeloperoxidase during inflammation, despite the presence in the blood of an excess of ceruloplasmin which inhibits its activity, is possible due to the partial proteolysis of ceruloplasmin by thrombin. In this study, the myeloperoxidase-ceruloplasmin-thrombin heterohexamer was obtained in vitro. The building of a heterohexamer full-atomic model in silico, considering the glycosylation of the constituent proteins, confirmed the absence of steric barriers for the formation of protein-protein contacts. It was shown that the partial proteolysis of ceruloplasmin does not affect its ability to bind to myeloperoxidase, and a structural model of the heterohexamer was obtained using the small-angle neutron scattering method.

Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm.

Ultra-low-power is a key performance indicator in 6G-IoT ecosystems. Sensor nodes in this eco-system are also capable of running light-weight artificial intelligence (AI) models. In this work, we have achieved high performance in a gas sensor system using Convolutional Neural Network (CNN) with a smaller number of gas sensor elements. We have identified redundant gas sensor elements in a gas sensor array and removed them to reduce the power consumption without significant deviation in the node's performance. The inevitable variation in the performance due to removing redundant sensor elements has been compensated using specialized data pre-processing (zero-padded virtual sensors and spatial augmentation) and CNN. The experiment is demonstrated to classify and quantify the four hazardous gases, viz., acetone, carbon tetrachloride, ethyl methyl ketone, and xylene. The performance of the unoptimized gas sensor array has been taken as a "baseline" to compare the performance of the optimized gas sensor array. Our proposed approach reduces the power consumption from 10 Watts to 5 Watts; classification performance sustained to 100 percent while quantification performance compensated up to a mean squared error (MSE) of 1.12 × 10-2. Thus, our power-efficient optimization paves the way to "computation on edge", even in the resource-constrained 6G-IoT paradigm.

Effect of alpha-lactalbumin and lactoferrin oleic acid complexes on chromatin structural organization.

This work focuses on the study of multimeric alpha-lactalbumin oleic acid and lactoferrin oleic acid complexes. The purpose of the research is to study possible mechanisms involved in their pro-apoptotic activities, as seen in some tumor cell cultures. Complexes featuring oleic acid (OA) with human alpha-lactalbumin (hAl) or with bovine alpha-lactalbumin (bAl), and human lactoferrin (hLf) were investigated using small-angle neutron scattering (SANS). It was shown that while alpha-lactalbumin protein complexes were formed on the surface of polydisperse OA micelles, the lactoferrin complexes comprised a monodisperse system of nanoscale particles. Both hAl and hLf complexes appeared to interact with the chromatin of isolated nuclei affecting chromatin structural organization. The possible roles of these processes in the specific anti-tumor activity of these complexes are discussed.

A double-edged sword: supramolecular complexes of triazavirine display multicenter binding effects which influence aggregate formation.

Communicated by Ramaswamy H. Sarma.

The amyloidogenicity of the influenza virus PB1-derived peptide sheds light on its antiviral activity.

The influenza virus polymerase complex is a promising target for new antiviral drug development. It is known that, within the influenza virus polymerase complex, the PB1 subunit region from the 1st to the 25th amino acid residues has to be is in an alpha-helical conformation for proper interaction with the PA subunit. We have previously shown that PB1(6-13) peptide at low concentrations is able to interact with the PB1 subunit N-terminal region in a peptide model which shows aggregate formation and antiviral activity in cell cultures. In this paper, it was shown that PB1(6-13) peptide is prone to form the amyloid-like fibrillar aggregates. The peptide homo-oligomerization kinetics were examined, and the affinity and characteristic interaction time of PB1(6-13) peptide monomers and the influenza virus polymerase complex PB1 subunit N-terminal region were evaluated by the SPR and TR-SAXS methods. Based on the data obtained, a hypothesis about the PB1(6-13) peptide mechanism of action was proposed: the peptide in its monomeric form is capable of altering the conformation of the PB1 subunit N-terminal region, causing a change from an alpha helix to a beta structure. This conformational change disrupts PB1 and PA subunit interaction and, by that mechanism, the peptide displays antiviral activity.

Triazavirine supramolecular complexes as modifiers of the peptide oligomeric structure.

In this study, we present molecular dynamics simulations of the antiviral drug triazavirine, that affects formation of amyloid-like fibrils of the model peptide (SI). According to our simulations, triazavirine is able to form linear supramolecular structures which can act as shields and prevent interactions between SI monomers. This model, as validated by simulations, provides an adequate explanation of triazavirine's mechanism of action as it pertains to SI peptide fibril formation.

Partially Assembled Nucleosome Structures at Atomic Detail.

The evidence is now overwhelming that partially assembled nucleosome states (PANS) are as important as the canonical nucleosome structure for the understanding of how accessibility to genomic DNA is regulated in cells. We use a combination of molecular dynamics simulation and atomic force microscopy to deliver, in atomic detail, structural models of three key PANS: the hexasome (H2A·H2B)·(H3·H4)2, the tetrasome (H3·H4)2, and the disome (H3·H4). Despite fluctuations of the conformation of the free DNA in these structures, regions of protected DNA in close contact with the histone core remain stable, thus establishing the basis for the understanding of the role of PANS in DNA accessibility regulation. On average, the length of protected DNA in each structure is roughly 18 basepairs per histone protein. Atomistically detailed PANS are used to explain experimental observations; specifically, we discuss interpretation of atomic force microscopy, Förster resonance energy transfer, and small-angle x-ray scattering data obtained under conditions when PANS are expected to exist. Further, we suggest an alternative interpretation of a recent genome-wide study of DNA protection in active chromatin of fruit fly, leading to a conclusion that the three PANS are present in actively transcribing regions in a substantial amount. The presence of PANS may not only be a consequence, but also a prerequisite for fast transcription in vivo.

Structure of RecX protein complex with the presynaptic RecA filament: Molecular dynamics simulations and small angle neutron scattering.

Using molecular modeling techniques we have built the full atomic structure and performed molecular dynamics simulations for the complexes formed by Escherichia coli RecX protein with a single-stranded oligonucleotide and with RecA presynaptic filament. Based on the modeling and SANS experimental data a sandwich-like filament structure formed two chains of RecX monomers bound to the opposite sides of the single stranded DNA is proposed for RecX::ssDNA complex. The model for RecX::RecA::ssDNA include RecX binding into the grove of RecA::ssDNA filament that occurs mainly via Coulomb interactions between RecX and ssDNA. Formation of RecX::RecA::ssDNA filaments in solution was confirmed by SANS measurements which were in agreement with the spectra computed from the molecular dynamics simulations.