Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area is the assessment of drug interactions. The AI analysis of large datasets, such as drugs’ chemical structure, pharmacological properties, molecular pathways, and known interaction patterns, can provide mechanistic insights and identify potential associations by integrating all this complex information and returning potential risks associated with these interactions. In this context, an area where AI may prove valuable is in the assessment of the underlying mechanisms of drug interactions with natural products (i.e., herbs) that are used as dietary supplements. These products pose a challenging problem since they are complex mixtures of constituents with diverse and limited information regarding their pharmacological properties, especially their pharmacokinetic data. As the use of herbal products and supplements continues to grow, it becomes increasingly important to understand the potential interactions between them and conventional drugs and the associated adverse drug reactions. This review will discuss AI approaches and how they can be exploited in providing valuable mechanistic insights regarding the prediction of interactions between drugs and herbs, and their potential exploitation in experimental validation or clinical utilization.

Easily and flexibly deployable ad-hoc communication networks emerging in tactical military or even civilian contexts, frequently suffer from poor synchronization due to lack of coordinating infrastructure. In addition to synchronization issues, and especially in military settings, security from the aspect of detectability is also of crucial importance. Imperfect synchronization can be dealt with by making use of Quasi-Synchronous Code Division Multiple Access (QS-CDMA), relying on Loosely Synchronous Codes to maintain orthogonality in the presence of limited time delays. Security, in terms of low probability of detection (LPD) from the standpoint of a malicious adversary, can be improved (reduced detection) by employing randomization techniques that disrupt the inherent structure of the transmitted QS-CDMA signals. This is based on the fact that QS-CDMA signals are Cyclostationary, having (almost) periodic Auto-Correlation functions (ACF) due to eminent signal periodicities (such as spreading code repetition). In this paper, we propose techniques to disturb the ACF and equivalently the Spectral Correlation function, and reduce the Degree of Cyclostationarity (DCS), our LPD measure. Specifically, we investigate randomization via 1) random per symbol time dithering and 2) random selection of spreading sequences, as well as a hybrid approach combining time dithering and code randomization. In all proposed techniques knowledge of the randomization pattern is not required at the legitimate receiver. We derive the Spectral Correlation function of the QS-CDMA signal under the proposed randomization schemes and compare it to simulations. We show through analysis and extensive numerical simulations that the proposed technique can reduce the DCS by almost two orders of magnitude. We also show that enhanced LPD can be achieved using the proposed techniques while sacrificing a part of the reduced time synchronization requirement. We further analyze the implications of the friendly receiver not knowing the randomization pattern and present results on the resulting communication performance.

This letter investigates reduction in the degree of cyclostationarity (DCS) of digital communication signals in the presence of polarization diversity. Reduction in DCS decreases the probability of detection of an intruder that searches for periodic patterns in the transmitted signal. We propose a DCS reducing scheme that exploits dual-polarization diversity when correlated wide sense stationary information streams with different rates and codes are transmitted over polarizations assuming a uni-polarized adversary. The effects of depolarization and data correlation across polarizations on both the DCS experienced by the adversary, and the resulting probability of detection are investigated.

Internet of Things (IoT) applications are anticipated to bloom in the future, and two popular industry areas related to Internet of Things (IoT) are smart houses and smart factories. To make a home or factory smart, hundreds or thousands of sensors need to work and communicate together with simple, secure and stable connectivity. Thus, the design of smart homes and smart factories relies on wireless sensor networks, whose transmission is often characterized to be sporadic. Classical approaches to multiuser communications rely on tight time synchronization achieved by employing coordinating infrastructure, but in a mass sensor transmission system, sensors may communicate with each other directly without relying on coordinating mechanisms. Our study explores the possibilities of simple and reliable multiuser detection in DS-CDMA system for few data and short distance mass sensor connectivity with joint synchronization of data. Furthermore, we discussed a scenario where spreading sequences are overused. We show that our system has strong potential for excellent mass sensors detection performance without synchronizing infrastructure.

Respiratory rate (RR) is an indicator of various psychological and pathological conditions and needs to be monitored easily and reliably. Emerging trends and recent advancements indicate that wearables are the future of continuous noninvasive vital measurement for healthcare. Numerous recent studies have focused on estimating RR from the electrocardiogram (ECG) or photoplethysmogram (PPG), which can be acquired non-invasively with minimal hardware and without obstructing spontaneous breathing patterns. While easy and flexible in acquisition, such signals may suffer from poor signal quality, which in turn results in unreliable estimation of RR, an issue that can be addressed by evaluating the quality of the obtained estimates. In this work, we propose a novel method for RR estimation based on the cross-correlation function that demonstrates improved estimation properties compared to most existing open-source methods achieving 0-bias and limits of agreement (LOAs) from -5.37 to 5.44 bpm. We propose the use of variance as an estimate quality metric and demonstrate how this approach results in higher accuracy. Two types of fusion are designed and evaluated, stressing the importance of quality evaluation and estimate combination in improving accuracy, reaching LOAs between -1.44 and 0.9 bpm. Finally, we stress the importance of signal duration in estimation accuracy, and demonstrate how longer segments result in lower error, validating this finding on multiple open-source methods. Our results are evaluated on three publicly available datasets, including CapnoBase, a benchmark database on respiration.

Variability in single neuron responses presents a challenge in establishing reliable representations of visual stimuli essential for driving behavior. To enhance accuracy, integration of responses from multiple neurons is imperative. This study leverages simultaneous recordings from a large population (tens of hundreds) of neurons, achieved through in vivo mesoscopic 2-photon calcium imaging of the primary visual cortex (V1) in mice, under visual stimulus conditions as well as in resting state (absence of stimulus). The visual stimulus consisted of 16 distinct randomly shuffled directions of motion presented to the mice. We employed mutual information to identify neurons that contain the most significant information about the stimulus direction. As expected, neurons displaying high predictive power (HPP) in stimulus decoding exhibit elevated firing event rates during stimulus presentation. Furthermore, functional connectivity among HPP neurons during visual stimulation is denser and stronger compared to functional connectivity among other visually responsive neurons. Functional connections among HPP neurons appear to form independently of distance, suggesting a distributed yet highly coordinated network. In contrast, HPP neuronal activity and functional connectivity differed significantly at resting state. Specifically, during the resting state, HPP neurons exhibited lower event rates and functional connectivity structure that was not significantly different from that of other visually responsive neurons. This suggests that HPP neurons are less susceptible to being driven simultaneously by internal brain states in the absence of a stimulus. Finally, the tuning properties of HPP neurons were unexpectedly diverse: while some were sharply tuned, others conveyed a similar amount of mutual information, despite exhibiting much weaker tuning. This study sheds light on the organization of neuronal ensembles important for decoding visual motion direction in mouse area V1, contributing to the understanding of information processing in mouse visual cortex.