Mechanism Of Information Storage Research Articles

The possibilities of big data in pharmacoepidemiology, problems of use, legal regulation

The growth of digitalization in medicine has significantly expanded the possibilities of using big data in pharmacoepidemiology. The use of big data makes it possible to reduce the cost of clinical research, increase the speed of recruitment and expand the sample, makes it possible to evaluate the effect of drugs in pregnant women and effectiveness in rare diseases. The databases PubMed, Scopus, Web of Science and Google Scholar for 12 years from 2012 to 2023, United Nations documents, World Health Organization, Federal laws of the Russian Federation in the field of artificial intelligence, protection of confidential information, and clinical research were analyzed. The search was carried out by keywords: «big data», «registers», «pharmacoepidemiology», «personal data», «legal regulation», «protection methods». The article provides examples of the use of big data in healthcare, including from 25 to 50 million people. The analysis of the literature data revealed the same type of problems – the lack of uniformity in the introduction of information, incomplete information, limited availability. The review identifies the problems of protecting the confidentiality of information. The mechanisms of information standardization, storage, and data processing are considered. The international and Russian legislative framework regulating the conduct of clinical trials using big data is presented.

Algebraic and toroidal representation of the genetic code

The genetic code is a set of regulatory principles that control the translation of information encoded in messenger RNA (mRNA) into a sequence of amino acids. This study proposes a model starting from the relationships between its physicochemical properties of nucleobase in the codons. We employed a binary metric and the Kronecker product to represent the physicochemical properties of nucleobases in a vector space. This state space has a hierarchical order, with self-similarity and symmetry properties in the hydrogen bonds of triplets. The state space can be mapped under linear transformations to a toroidal geometry with allometric properties. Furthermore, this geometric representation highlights a charge symmetry that exists in amino acids and in the distribution of essential and non-essential amino acids. These results describe a state space between codon-anticodon interactions that can be interpreted as Bell states. This suggests that there is a quantum phenomenon involved in the mechanisms of information storage in DNA. In addition, toroidal geometry can be used to represent the sequences of codons of the mRNA that encode the sequence of amino acids of the proteins to find similarities or homologies between evolutionary related species.

Single gated RNN with differential weighted information storage mechanism and its application to machine RUL prediction

The full-life data of machine is complex and abundant, requiring specialized and deep predictive models for accurate forecasts. However, achieving high prediction accuracy often increases model complexity, hindering edge deployment. To address this, several lightweight regression operators named single gated recurrent neural networks have been first proposed, striking a balance between accuracy and simplicity, and exploring the contribution of different gates in RUL prediction. In addition, during the whole degeneration process of machines, there exists global tendency and local vibration, different trends should be learned differentially. Thus, a novel lightweight differential learning mechanism called differential weighted information storage mechanism is proposed, which adopts different weight updated rules to make the weights store different trend information without any parameters added. Based on the above improvement, several lightweight single gated recurrent neural networks with the differential weighted information storage mechanism are first proposed. Then, deep learning frameworks are constructed by the proposed operators and adopted in gears and aero-engines RUL prediction. The experiment results show the outperformance of the proposed methods in accuracy and computation burden compared with recent works.

The organizational principles of impression formation

Trait impressions about others are a fundamental tool to navigate the rich social environment and yet a unitary model of its organizational principles is still lacking. The statistical properties of impression formation observed in previous studies are akin to processes that govern information encoding and storage in memory, suggesting similar cognitive and computational mechanisms. Here, in 2,780 participants, impression formation has been formalized with a computational model representing three organizational principles of memory (temporal, semantic and valence-related). The model specifically captured two main patterns of impression formation: (1) a negative valence effect that makes negative impressions loom longer than positive ones; (2) an interaction effect between the temporal and valence content that endorses more negative impressions when negative information is met first. This work shows that mechanisms of information encoding, storage and retrieval interact in ways that explain biased impression formation about social partners, thereby providing quantitative evidence for those mechanisms in individuals’ impressions of others’ social qualities. We discuss the implications of these results for social impressions in different, real-world contexts, and suggest how the proposed model might be extended to capture other kinds of effects, from negativity bias and pessimism to social discrimination.

Production Data Management of Smart Farming Based on Shili Theory

The development of smart farming comes with a lot of data problems. Studies have shown this is due to insufficient cognition of the structural relationship between data and events. Shili Theory is an attractive concept. To embed intelligent agricultural technology in events and the natural environment, especially to unify and standardize agricultural production data, firstly, this paper has defined the concept of Shili Theory which researches the natural regularity of the event by Shili Mirrored Structure. Secondly, this paper has proposed a Shili Mirrored Structure based on the technology development path (from the human brain memory mechanism to the information storage mechanism to intelligent technology). Finally, the structure has been applied to develop an intelligent system of agricultural production data management. In rice production of Jilin Province, it forms the event chain of the whole plant 5T (seed, seeding, paddy shoot, grain, product period operation) and grain period 5T (harvesting, field stacking, drying, warehousing, storing). The system application shows that this management structure can reduce data flow, improve data utilization, and enhance the correlation between data and events. It can realize the quality improvement of the agricultural production process, especially revealing the 8.83% significant latent loss in rice harvest.

Non-complementary strand commutation as a fundamental alternative for information processing by DNA and gene regulation.

The discovery of the DNA double helix has revolutionized our understanding of data processing in living systems, with the complementarity of the two DNA strands providing a reliable mechanism for the storage of hereditary information. Here I reveal the 'strand commutation' phenomenon-a fundamentally different mechanism of information storage and processing by DNA/RNA based on the reversible low-affinity interactions of essentially non-complementary nucleic acids. I demonstrate this mechanism by constructing a memory circuit, a 5-min square-root circuit for 4-bit inputs comprising only nine processing ssDNAs, simulating a 572-input AND gate (surpassing the bitness of current electronic computers), and elementary algebra systems with continuously changing variables. Most importantly, I show potential pathways of gene regulation with strands of maximum non-complementarity to the gene sequence that may be key to the reduction of off-target therapeutic effects. This Article uncovers the information-processing power of the low-affinity interactions that may underlie major processes in an organism-from short-term memory to cancer, ageing and evolution.

Luminescence principle and characteristics of imaging plate

The imaging plate (IP) is a reusable detector for detecting radiation particles in a complex electromagnetic field environment, and it is widely used as a detection medium in laser-accelerated particle beam diagnostic equipment. Therefore, it is necessary to study the performance characteristics and physical mechanism of IP. An electron source with known activity is used to explore the performance characteristics of IP. A <sup>90</sup>Sr/<sup>90</sup>Y electron source is used to measure the time attenuation curve, calibrate the absolute sensitivity, and study the law of multiple scanning of BAS-SR and BAS-TR. In the case of a longer irradiation, the fading cannot be neglected, and the attenuation curves are modified. The time attenuation characteristics indicate that the IP should be cooled after irradiation, and the scanning should be carried out in the slow decay process to reduce the influence of the reading error in the decay process. The absolute sensitivity of BAS-SR and BAS-TR to <sup>90</sup>Sr/<sup>90</sup>Y source are (0.033±0.002) PSL/<i>e</i> and (0.018±0.0038) PSL/<i>e</i> (photostimulated light, PSL), respectively, which are consistent with the results of most absolute sensitivity. The absolute sensitivity is closely related to the type of IP, scanning equipment, and experimental environment. In addition, the energy spectrum integral effect of the broad spectrum <i>β</i> source has a significant influence on the absolute sensitivity. This method is only suitable for the rough evaluation of the sensitivity characteristic parameters of the IP. Multiple scanning approximately satisfies the double exponential function distribution, which is consistent with the physical model. The characteristics of IP are determined by its storage principle. The fluorescence layer of IP is composed of typical electron trapping materials <i>M</i>F<i>X</i> (<i>M</i> = Ca, Sr, Ba; <i>X</i> = C1, Br, I) alkaline earth metal fluorhalide BaFBr. When the IP is irradiated, a large number of free electron-hole pairs are excited by the deposited energy in the material, and the free electrons will be captured by the electron trap, so the fluorescence layer of the IP records the radiation particles’ information through the energy deposited. In this paper, we study three kinds of models. Based on the models, a photo-stimulated luminescence model is proposed to describe the electron transfer process. The photo-stimulated luminescence model describes the physical mechanism of energy deposition, information storage, and information scanning of radiation particles. The relationship between the physical mechanism and characteristics is explained effectively by combining the microscopic mathematical model with the macroscopic physical phenomenon. It provides a specific data basis for the subsequent application of IPs in laser plasma diagnostic experiments.

BK channels at dendritic spines: A mechanism for coupling morphology, plasticity and information storage?

BK channels at dendritic spines: A mechanism for coupling morphology, plasticity and information storage?

Evolution of Nb-Ta Anodic Memristors Identified By Combinatorial Screening

The conventional memory technology, based on von Neumann architecture, has challenged technical and scaling limitations. The architectures with separated memory and computing units have resulted in the low efficiency of computing systems. Hence, huge scientific attention has been recently drawn into the development of novel systems using the in-memory computing concept. Memristors are foreseen as a type of non-volatile memories that can fulfill such requirements but also as electronic components that can solve sneak path currents problems while consuming low power. The information storage mechanism is based on the resistance change of memristors which is driven by an applied external electric field. With this regard, the memory medium plays a crucial role, being an oxide layer sandwiched between the metallic bottom and top electrode. Valve metals and their oxides have shown promising switching properties when used as elements of metal-insulator-metal memristive (MIM) structures.The switching mechanism still depends on the nano-dimensional conductive filaments formation inside the oxide. Even though devices based on Hf, Nb, Ti, and Ta demonstrated a long lifetime, high data retention or resistive state ratio, devices are suffering from low lifetimes in the order of several thousand switching cycles, mainly due to the unpredictable size and position of conductive filaments being affected by the movement of cations or O species. Therefore, the development of materials for defect-engineered memristive applications is crucial. This was the main motivation for this study supported by the fact that memristors based on pure valve metals exhibited promising switching behavior. In this work, devices based on Ta, Nb, and their alloys were investigated. Since the goal was to improve the properties of devices based on pure metals, Nb and Ta were mixed and their alloys were used as bottom electrode for anodic memristors. It can be inferred that by mixing Ta and Nb, multifunctional devices can be produced.High number of Nb-Ta alloys were co-deposited by sputtering onto previously oxidized Si wafers. Afterwards, the samples were anodized for growing an insulating active layer for memristive devices. Onto the so-formed mixed oxides, Pt was deposited as top electrode. The compositional gradient was mapped by a scanning energy-dispersive X-ray spectroscopy (SEDX) system. The total compositional spread of the Nb-Ta library was ranging from Nb - 13 at.%Ta to Nb – 80 at% Ta over three Si wafers. Memristive devices based on Nb or Ta were also fabricated in identical conditions and used as reference samples. By using a combinatorial approach, the investigation of various electrical parameters could be established for more than 300 memristive devices produced per Si wafer. Electrical characterization was performed by a self-developed Gantry robot controlled via Labview software specifically designed for basic tests required for memristive characterization (I-U sweeps, endurance, retention tests). Electrical properties were screened with a compositional resolution of 1 at.%.The results have shown that memristive devices based on Ta reached a long lifetime and high resistive state ratio, whereas devices based on Nb indicated multi-level switching as well as non-volatile and threshold switching characteristics. The formation of Ta-oxyphosphate, responsible for conductive filaments pining, was confirmed by hard X-ray photoelectron spectroscopy. Similarly, multi-level switching characteristics were explained by the incorporation of electrolyte species in Nb2O5. However, Nb devices did not show reversible switching from unipolar to bipolar mode and vice versa. The improvement regarding reversible switching between unipolar and bipolar mode has been observed for memristive devices based on alloys containing Nb -35 at. % Ta. These memristors have also demonstrated longer endurance and data retention as compared to devices based on pure Nb and Ta. The switching mechanism was investigated by conductive filaments imaging using transmission electron microscopy.Conclusively, anodic memristors based on Nb and Ta exhibited non-volatile and threshold characteristics which can spread the range of memristive applications. Reversible switching behavior and multi-level switching possibilities are relevant for the development of memristive synapses and neuromorphic systems. The anodic growth of an insulating layer for devices based on Nb, Ta, and Nb/Ta alloys has simplified, speed up and decreased the cost of fabrication processes. The performance of anodic memristors based on metallic thin films containing Nb -35 at. % Ta indicated a substantial improvement. The switching mechanisms could be suggested based on the nanostructures found in oxide revealing the position and size of conductive filaments.I. Zrinski et al., Appl. Surf. Sci., 548, 149093 (2021) https://doi.org/10.1016/j.apsusc.2021.149093.I. Zrinski, et al., Appl. Mater. Today, accepted, (2021).I. Zrinski, et. al, J. Phys. Chem. Lett., 12, 8917 (2021) https://doi.org/10.1021/acs.jpclett.1c02346 Figure 1

Spike-timing-dependent plasticity rewards synchrony rather than causality.

Spike-timing-dependent plasticity (STDP) is a candidate mechanism for information storage in the brain, but the whole-cell recordings required for the experimental induction of STDP are typically limited to 1 h. This mismatch of time scales is a long-standing weakness in synaptic theories of memory. Here we use spectrally separated optogenetic stimulation to fire precisely timed action potentials (spikes) in CA3 and CA1 pyramidal cells. Twenty minutes after optogenetic induction of STDP (oSTDP), we observed timing-dependent depression (tLTD) and timing-dependent potentiation (tLTP), depending on the sequence of spiking. As oSTDP does not require electrodes, we could also assess the strength of these paired connections three days later. At this late time point, late tLTP was observed for both causal (CA3 before CA1) and anticausal (CA1 before CA3) timing, but not for asynchronous activity patterns (Δt = 50 ms). Blocking activity after induction of oSTDP prevented stable potentiation. Our results confirm that neurons wire together if they fire together, but suggest that synaptic depression after anticausal activation (tLTD) is a transient phenomenon.

Distributed memory of neural networks and the problem of the intelligence`s essence

The question of the nature of the distributed memory of neural networks is considered. Since the memory capacity of a neural network depends on the presence of feedback in its structure this question requires further study. It is shown that the neural networks without feedbacks can be exhaustively described based on analogy with the algorithms of noiseproof coding. For such networks the use of the term "memory" is not justified at all. Moreover, functioning of such networks obeys the analog of Shannon formula, first obtained in this paper. This formula allows to specify in advance the number of images that a neural network can recognize for a given code distance between them. It is shown that in the case of artificial neural networks with negative feedback it is really justified to talk about a distributed memory network. It is also shown that in this case the boundary between distributed memory of a neural network and information storage mechanisms in such elements as RS-triggers is diffuse. For the given example a specific formula is obtained, which connects the number of possible states of outputs of the network (and, hence, the capacity of its memory) with the number of its elements.

Regulation of Hippocampal Gamma Oscillations by Modulation of Intrinsic Neuronal Excitability.

Ion channels activated around the subthreshold membrane potential determine the likelihood of neuronal firing in response to synaptic inputs, a process described as intrinsic neuronal excitability. Long-term plasticity of chemical synaptic transmission is traditionally considered the main cellular mechanism of information storage in the brain; however, voltage- and calcium-activated channels modulating the inputs or outputs of neurons are also subjects of plastic changes and play a major role in learning and memory formation. Gamma oscillations are associated with numerous higher cognitive functions such as learning and memory, but our knowledge of their dependence on intrinsic plasticity is by far limited. Here we investigated the roles of potassium and calcium channels activated at near subthreshold membrane potentials in cholinergically induced persistent gamma oscillations measured in the CA3 area of rat hippocampal slices. Among potassium channels, which are responsible for the afterhyperpolarization in CA3 pyramidal cells, we found that blockers of SK (KCa2) and KV7.2/7.3 (KCNQ2/3), but not the BK (KCa1.1) and IK (KCa3.1) channels, increased the power of gamma oscillations. On the contrary, activators of these channels had an attenuating effect without affecting the frequency. Pharmacological blockade of the low voltage-activated T-type calcium channels (CaV3.1–3.3) reduced gamma power and increased the oscillation peak frequency. Enhancement of these channels also inhibited the peak power without altering the frequency of the oscillations. The presented data suggest that voltage- and calcium-activated ion channels involved in intrinsic excitability strongly regulate the power of hippocampal gamma oscillations. Targeting these channels could represent a valuable pharmacological strategy against cognitive impairment.

Integration of communication and computing in blockchain-enabled multi-access edge computing systems

Blockchain and multi-access edge computing (MEC) are two emerging promising technologies that have received extensive attention from academia and industry. As a brand-new information storage, dissemination and management mechanism, blockchain technology achieves the reliable transmission of data and value. While as a new computing paradigm, multi-access edge computing enables the high-frequency interaction and real-time transmission of data. The integration of communication and computing in blockchain-enabled multi-access edge computing networks has been studied without a systematical view. In the survey, we focus on the integration of communication and computing, explores the mutual empowerment and mutual promotion effects between the blockchain and MEC, and introduces the resource integration architecture of blockchain and multi-access edge computing. Then, the paper summarizes the applications of the resource integration architecture, resource management, data sharing, incentive mechanism, and consensus mechanism, and analyzes corresponding applications in real-world scenarios. Finally, future challenges and potentially promising research directions are discussed and present in detail.

From synapse to network: models of information storage and retrieval in neural circuits.

The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this informationand that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental dataand in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations invivo during delayed response tasks.

Exploring Brain Information Storage/Reading for Neuronal Connectivity Using Macromolecular Electrochemical Sensing Motors

Dense electroactive gels of conducting polymers, carbon nanotubes, or graphenes, constituted by multisensing macromolecular electrochemical motors, are presented herein as model materials to get a physicochemical characterization of the opening/closing cycles of ion protein channels in neurons. The conformational contracted energetic states of these macromolecular motors store both long‐term and short‐term information packages. Under a potential cycle, the energy of each ionic pulse generated during the channel opening reads the stored information, which is restored during the channel conformational closing. Simultaneously the motor actuation senses the working physical and chemical energetic conditions, transferring to the ionic pulse energy those sensing information packages. A quantitative description of the stored and read information packages is attained from basic electrochemical, mechanical, and polymeric concepts. Translated to ion channel proteins in brain dendrites, it can describe brain information storage mechanisms and the energetic information packages read and carried through each ionic pulse to the action potential. An unexplored way is opened to try to understand and describe how the different brain parts can use this information to originate brain functions. In parallel, the construction of new electrochemical devices and computers replicating biological functions will provide feedback quantitative information to decipher brain functions.

Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish.

The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations.

Possibility of information encoding/decoding using the memory effect in fractional-order capacitive devices

In this study, we show that the discharge voltage pattern of a supercapacitor exhibiting fractional-order behavior from the same initial steady-state voltage into a constant resistor is dependent on the past charging voltage profile. The charging voltage was designed to follow a power-law function, i.e. v_c(t)=V_{cc} left( {t}/{t_{ss}}right) ^p ;(0<t leqslant t_{ss}), in which t_{ss} (charging time duration between zero voltage to the terminal voltage V_{cc}) and p (0<p<1) act as two variable parameters. We used this history-dependence of the dynamic behavior of the device to uniquely retrieve information pre-coded in the charging waveform pattern. Furthermore, we provide an analytical model based on fractional calculus that explains phenomenologically the information storage mechanism. The use of this intrinsic material memory effect may lead to new types of methods for information storage and retrieval.

E-VOTING SYSTEMS USING BLOCKCHAIN: A SYSTEMATIC REVIEW AND FUTURE RESEARCH DIRECTION

One of the most important discoveries and creative developments that is playing a vital role in the professional world today is blockchain technology. A blockchain is a distributed, digitized and consensus-based secure information storage mechanism. Blockchain technology moves in the direction of persistent revolution and change. In the last couple of years, the upsurge in blockchain technology has obliged scholars and specialists to scrutinize new ways to apply blockchain technology with a wide range of domains. The dramatic increase in blockchain technology has provided many new application opportunities, including e-voting application. The present article provides a systematic review of emerging blockchain-based e-voting systems. In this paper, we call attention to the open research matters in this fast-growing field, explaining them in some details. It was concluded that frameworks needed enhancements in order to be used in voting systems due to these reservations. KEYWORDS: blockchain, e-voting, cloud computing, ethereum; ballot

Lysine-specific acetylated proteome from the archaeon Thermococcus gammatolerans reveals the presence of acetylated histones

Thermococcus gammatolerans EJ3 is an extremophile archaeon which was revealed as one of the most radioresistant organisms known on Earth, withstanding up to 30 kGy gamma-ray radiations. While its theoretical proteome is rather small, T. gammatolerans may enhance its toolbox by post-translational modification of its proteins. Here, we explored its extent of Nε-acetylation of lysines. For this, we immunopurified with two acetylated-lysine antibodies the acetylated peptides resulting from a proteolysis of soluble proteins with trypsin. The comparison of acetylated proteomes of two archaea highlights some common acetylation patterns but only 4 out of 26 orthologous proteins found to be acetylated in both species, are acetylated on the same lysine site. We evidenced that histone B is acetylated in T. gammatolerans at least at two different sites (K27 and K36), and a peptide common at the C-terminus of histones A and B is also acetylated. We verified that acetylation of histones is a common trait among Thermococcales after recording data on Thermococcus kodakaraensis histones and identifying three acetylated sites. This discovery reinforces the strong evolutionary link between Archaea and Eukaryotes and should be an incentive for further investigation on the extent and role of acetylation of histones in Archaea. SignificanceAcetylation is an important post-translational modification of proteins that has been extensively described in Eukaryotes, and more recently in Bacteria. Here, we report for the first time ever that histones in Archaea are also modified by acetylation after a systematic survey of acetylated peptides in Thermococcus gammatolerans. Structural models of histones A and B indicates that acetylation of the identified modified residues may play an important role in histone assembly and/or interaction with DNA. The in-depth protein acetylome landscape in T. gammatolerans includes at least 181 unique protein sequences, some of them being modified on numerous residues. Proteins involved in metabolic processes, information storage and processing mechanisms are over-represented categories in this dataset, highlighting the ancient role of this protein post-translational modification in primitive cells.

Photon Activation of Glassy Dynamics: A Mechanism for Photoinduced Fluidization, Aging, and Information Storage in Amorphous Materials.

We discuss the photon activation of structural relaxations in glassy melts and frozen glasses containing molecules that can photoisomerize. The built-in stress following a photoinduced electronic transition lowers the thermal activation barrier for subsequent structural reconfiguration of the glassy matrix. We provide explicit predictions for the barrier distribution and structural relaxation spectrum as functions of the concentration of photoactivated molecules and the fragility of the material. The typical barrier decreases upon photoactivation, while the barrier distribution increases in width with increasing mole fraction of photoactive molecules and fluence, and becomes multimodal. In a frozen glass, the initial effects of photoisomerization locally facilitate the dynamics near the excited chromophores and can lead to complete fluidization at a sufficiently high fluence. Photon activation initially decreases the yield strength of the glass. Depending on the precise time course of illumination, there however emerges a spatial coexistence of softened regions with regions that, after being destabilized by illumination, have reconfigured so that they are now made of ultrastable glass or have crystallized as in a porcelain. This sequence of events, after illumination, can lead to highly stable amorphous solids, potentially approaching the Kauzmann limit. These mechanisms are at the root of optical information storage technologies in amorphous materials.