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Chaos computing provides a large number of functions from a single hardware. Large-scale reconfigurability can be achieved flexibly by tuning only a few parameters from a chaos-based computing system. Implementation of reconfigurable complex functions from a single chaos circuit can alleviate area and power concerns due to decreasing technology nodes. It is possible to make a multi-input multi-output complex instruction set using the chaos-generated functionalities where operations are more uniform than conventional implementations. Lack of uniformity in implementation of instructions in traditional computing system provides opportunity for attackers to reverse engineer based on side channel power analysis. In this paper, it is proposed that chaos-based implementation of a complex instruction set is immune to classification-based reverse engineering attack. Cross obfuscation and self obfuscation schemes are proposed in this work which leverage reconfigurability of chaotic system for obfuscating the power profile of the instruction set and it has been made immune to reverse engineering attacks. The design utilizes 3-input multi-output instructions by using a single chaotic iterative map. We analyzed the immunity of this design against classification-based reverse engineering attack for six different classification algorithms with five dimensionality reduction techniques.

Gallic acid (GA) is evident to inhibit the platelet activation process, which plays an important role in preventing and activating blood clots and clot lysis. Moreover, GA has antioxidant, anti-inflammatory, and membrane-protecting capacities. The purpose of this investigation is to determine the clot-lysing and membrane-protecting capacity of GA using in vitroand in silicoapproaches. For this, we used an in vitroclot lysis and hemolyzing test to check its anti-atherothrombosis and membrane-stabilizing properties, respectively. Additionally, docking inquiries were conducted against cyclooxygenase-1 (COX-1) and plasminogen enzymes. Results suggest that GA exhibited concentration-dependent clot-lysing and membrane-protecting potentials. The IC50values for clot lysis and membrane stabilization were 136.17 ± 1.08 and 55.29 ± 1.09 μg/mL, respectively. It showed binding affinities with COX-1 (−6.8 kcal/mol) and plasminogen (−6.4 kcal/mol). Taken together, GA showed moderate clot-lysing and robust membrane-stabilizing effects, possibly through COX-1 and plasminogen interaction pathways. GA might be a good therapeutic agent for atherothrombosis and membrane damage, highlighting the need for additional researches into its underlying mechanisms in molecular level and potential clinical applications.

The citrus peel flavonoid tangeretin (TAN) has diverse biological activities, including antioxidant, anti-inflammatory, anti-tumor, hepatoprotective, and neuroprotective effects. This study emphasizes evaluating the anxiolytic effect of TAN on mice. Additionally, we conducted in silicoinvestigations to examine the potential molecular mechanisms behind the anxiolytic effect of TAN. For this, adult male Swissalbino mice were treated with TAN (10 and 20 mg/kg, p.o.) with or without the standard GABAergic agonist drug diazepam (DZP: 2 mg/kg, p.o.) and/or antagonist drug flumazenil (FLU: 0.1 mg/kg, i.p.) and checked for different locomotor behaviors using various mouse models. The molecular docking study of TAN was conducted against GABAAreceptor subunits. Findings suggest that TAN dose-dependently and significantly (p < 0.05) increased locomotor activities such as the number of field crosses, hole crosses, swings, and light residence time while decreasing the grooming and rearing parameters of the animals. With DZP, it significantly (p < 0.05) reduced the test parameters while altering these parameters with FLU. In molecular docking, TAN has a strong binding affinity of −6.6 and −7.0 kcal/mol for the α2 and α3 subunits of the GABAAreceptor, respectively, whereas the standard drugs DZP and FLU showed binding affinities between −6.0 and −6.7 kcal/mol for these subunits. MD simulations showed that TAN interacts with α3 subunits in a way similar to DZP, with similar RMSD and RMSF values and ligand properties. TAN also demonstrated acceptable drug-likeness and pharmacokinetics. Taken together, TAN augmented the anxiolytic effect of DZP while reducing the effect of FLU in mice.

The onset of the COVID-19 pandemic has disrupted food access, resulting in substantial consequences for food insecurity and contributing to adverse individual and public health outcomes. To comprehensively evaluate these challenges and grasp their implications for food security, this study aimed to evaluate the contributing determinants of food insecurity among rural households in the southwestern region of Bangladesh.

This brief presents four discrete-time chaotic map circuits, including three new map circuits and one that has been previously reported. The designs are hardware efficient as they each contain only four Metal Oxide Semiconductor (MOS) transistors. They offer robust chaotic performance with wide chaotic space and uniform entropic properties. The wide chaotic region is essential for applications where parametric perturbation can push the system out of the desirable chaotic region. The chaotic performance is analyzed using a bifurcation plot, Lyapunov exponent, correlation coefficient, sample entropy, and Shannon entropy, and improvement over previous transistor-level designs is demonstrated. The proposed method can be useful in applications such as random number generation, reconfigurable and flexible computing, side-channel attack mitigation, logic obfuscation, and chaos-based cryptography. A chaotic oscillator design is proposed and its application in a chaos-based reconfigurable logic gate is presented, as well.

Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10–3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.

The diterpenoid phytol is evidently acting against anemia in experimental animals. However, the molecular mechanisms behind this issue are yet to be discovered. This study aimed to evaluate phytol’s effect on methotrexate-induced folate-deficient animals through in vivo and in silico studies. For this, a total of thirty adult male Swiss albino mice were randomly divided into six different groups, namely the normal control (vehicle), the negative control (folate deficiency inducer, methotrexate 3 mg/kg), the standard (folic acid 1.5 mg/kg), two test groups comprising phytol 25 and 50 mg/kg, and a combined group composed of the standard and highest test doses of phytol. Except for the vehicle, all groups were treated with methotrexate for the first 3 days (once/day) to induce folate deficiency. Then followed by the respective treatment once a day for 3 days. Hemoglobin (Hb) level was measured from the peripheral blood (by tail cutting) on days 1st(before treatment), 4th(after methotrexate treatment), and 7th(after treatment). On the other hand, the computational studies were performed by PyMol, PyRex, Discovery Studio, and other complementary tools. Findings suggest that phytol significantly (p<0.05) augmented Hb levels that are altered by methotrexate-induced reduction of Hb levels in animals dose-dependently. The combination also augmented Hb levels in animals; however, its effect was slightly lower than the individual groups (standard and test). In the in silico study, phytol showed good binding capacity (binding energy: −7.0 kcal/mol) with dihydrofolate reductase (DHFR). In conclusion, phytol may act against folate deficiency by altering methotrexate’s impacts in animals, possibly through interacting with DHFR. Further validated research is necessary to develop phytol as an anti-anemia drug in the near future.

Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical substrates, including atomic switches, photonics, volatile memristors, and, recently, memcapacitors, to achieve efficient high-dimensional mapping. Traditional PRCs often consist of homogeneous device arrays, which rely on input encoding methods and large stochastic device-to-device variations for increased nonlinearity and high-dimensional mapping. These approaches incur high preprocessing costs and restrict real-time deployment. Here, we introduce a heterogeneous memcapacitor-based PRC that exploits internal voltage offsets to enable both monotonic and nonmonotonic input-state correlations crucial for efficient high-dimensional transformations. We demonstrate our approach’s efficacy by predicting a second-order nonlinear dynamical system with a low prediction error (1.80 × 10–4). Additionally, we predict a chaotic Hénon map, achieving a low normalized root-mean-square error (0.080). Unlike previous PRCs, such errors are achieved without input encoding methods, underscoring the power of distinct input-state correlations. Most importantly, we generalize our approach to other neuromorphic devices that lack inherent voltage offsets using externally applied offsets to realize various input-state correlations. Our approach and the unprecedented performance are major milestones toward high-performance full in-materia PRCs.

Ononin is a naturally occurring isoflavone glycoside generally found in various plants like soybeans, legumes, Chinese licorice, Mongolian milkvetch, kudzu, broccoli, and lupine. The compound has exhibited various therapeutic benefits in different laboratory tests. This study aims to evaluate pharmacological effects and biopharmaceutical features based on different clinical and preclinical studies and to assess the underlying mechanisms behind the effects against various deleterious diseases. For this, searching and collection of data were done (up to date as of October 07, 2023) in various reliable electronic databases, including PubMed/Medline, Scopus, Springer Link, ScienceDirect, Wiley Online, Web of Science, and Google Scholar. The findings exhibited beneficial effects against inflammation and a variety of diseases, such as neurodegenerative illnesses (Alzheimer's disease and cerebral I/R), viral and fungal infections (Influenza A and Candida albicans), diabetic nephropathy, osteoarthritis, obesity, and acute myocardial infraction (AMI). These beneficial effects are due to the antioxidant properties of the compound, which are capable of reducing ROS generation and the level of pro-inflammatory markers. The phytochemicals also demonstrated anticancer effects against various cancers, including breast, lung, orthotopic bone tumor, and laryngeal cancers. The compound exerted anticancer effects via cytotoxicity, an apoptotic effect by regulating the Bax/Bcl2 ratio, induction of oxidative stress, inhibition of proliferation, and metastasis through downregulating different cellular pathways such as the EGFR-Erk1/2, PI3K/Akt/mTOR, MEK/ERK, and JNK/ERK/p38 pathways. But the compound has poor oral bioavailability, resulting in low oral efficacy. Therefore, we suggest developing an alternative route and conducting extensive studies to understand the exact molecular mechanism, which will be more reliable for performing the next clinical trials to establish the lead as an efficacious therapeutic agent.

Solid-state neuromorphic systems based on transistors or memristors have yet to achieve the interconnectivity, performance, and energy efficiency of the brain due to excessive noise, undesirable material properties, and nonbiological switching mechanisms. Here we demonstrate that an alamethicin-doped, synthetic biomembrane exhibits memristive behavior, emulates key synaptic functions including paired-pulse facilitation and depression, and enables learning and computing. Unlike state-of-the-art devices, our two-terminal, biomolecular memristor features similar structure (biomembrane), switching mechanism (ion channels), and ionic transport modality as biological synapses while operating at considerably lower power. The reversible and volatile voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer creates conductive pathways that exhibit pinched current–voltage hysteresis at potentials above their insertion threshold. Moreover, the synapse-like dynamic properties of the biomolecular memristor allow for simplified learning circuit implementations. Low-power memristive devices based on stimuli-responsive biomolecules represent a major advance toward implementation of full synaptic functionality in neuromorphic hardware.