Information Exchange Research Articles

Complexity synchronization in living matter: a mini review

Fractal time series have been argued to be ubiquitous in human physiology and some of the implications of that ubiquity are quite remarkable. One consequence of the omnipresent fractality is complexity synchronization (CS) observed in the interactions among simultaneously recorded physiologic time series discussed herein. This new kind of synchronization has been revealed in the interaction triad of organ-networks (ONs) consisting of the mutually interacting time series generated by the brain (electroencephalograms, EEGs), heart (electrocardiograms, ECGs), and lungs (Respiration). The scaled time series from each member of the triad look nothing like one another and yet they bear a deeply recorded synchronization invisible to the naked eye. The theory of scaling statistics is used to explain the source of the CS observed in the information exchange among these multifractal time series. The multifractal dimension (MFD) of each time series is a measure of the time-dependent complexity of that time series, and it is the matching of the MFD time series that provides the synchronization referred to as CS. The CS is one manifestation of the hypothesis given by a “Law of Multifractal Dimension Synchronization” (LMFDS) which is supported by data. Therefore, the review aspects of this paper are chosen to make the extended range of the LMFDS hypothesis sufficiently reasonable to warrant further empirical testing.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

Bio-nanoparticles loaded with synovial-derived exosomes ameliorate osteoarthritis progression by modifying the oxidative microenvironment

Background and aimsOsteoarthritis (OA) is a prevalent degenerative joint disorder, marked by the progressive degeneration of joint cartilage, synovial inflammation, and subchondral bone hyperplasia. The synovial tissue plays a pivotal role in cartilage regulation. Exosomes (EXOs), small membrane-bound vesicles released by cells into the extracellular space, are crucial in mediating intercellular communication and facilitating the exchange of information between tissues. Our study aimed to devise a hydrogel microsphere infused with SOD3-enriched exosomes (S-EXOs) to protect cartilage and introduce a novel, effective approach for OA treatment.Materials and methodsWe analyzed single-cell sequencing data from 4247 cells obtained from the GEO database. Techniques such as PCR, Western Blot, immunofluorescence (IF), and assays to measure oxidative stress levels were employed to validate the cartilage-protective properties of the identified key protein, SOD3. In vivo, OA mice received intra-articular injections of S-EXOs bearing hydrogel microspheres, and the effectiveness was assessed using safranine O (S.O) staining and IF.ResultsSingle-cell sequencing data analysis suggested that the synovium influences cartilage via the exocrine release of SOD3. Our findings revealed that purified S-EXOs enhanced antioxidant capacity of chondrocytes, and maintained extracellular matrix metabolism stability. The S-EXO group showed a significant reduction in mitoROS and ROS levels by 164.2% (P < 0.0001) and 142.7% (P < 0.0001), respectively, compared to the IL-1β group. Furthermore, the S-EXO group exhibited increased COL II and ACAN levels, with increments of 2.1-fold (P < 0.0001) and 3.1-fold (P < 0.0001), respectively, over the IL-1β group. Additionally, the S-EXO group showed a decrease in MMP13 and ADAMTS5 protein expression by 42.3% (P < 0.0001) and 44.4% (P < 0.0001), respectively. It was found that S-EXO-containing hydrogel microspheres could effectively deliver SOD3 to cartilage and significantly mitigate OA progression. The OARSI score in the S-EXO microsphere group markedly decreased (P < 0.0001) compared to the OA group.ConclusionThe study demonstrated that the S-EXOs secreted by synovial fibroblasts exert a protective effect on chondrocytes, and microspheres laden with S-EXOs offer a promising therapeutic alternative for OA treatment.

Nociceptor‐Enhanced Spike‐Timing‐Dependent Plasticity in Memristor with Coexistence of Filamentary and Non‐Filamentary Switching

AbstractIn the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high‐energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low‐power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury‐enhanced spike‐time‐dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

Clinical and Genomic Features of Patients with Renal Cell Carcinoma and Advanced Chronic Kidney Disease: Analysis of a Multi-Institutional Database

Background: Patients with advanced chronic kidney disease (ACKD) are at an increased risk of developing renal cell carcinoma (RCC), but molecular alterations in RCC specimens arising from ACKD and overall survival (OS) in affected patients are not well defined. Patients and Methods: Using the Oncology Research Information Exchange Network (ORIEN) Total Cancer Care® protocol, 296 consented adult patients with RCC and somatic tumor whole exome sequencing were included. Patients with ACKD were defined as those with serum creatinine ≥1.5 mg/dL prior to RCC diagnosis. Results: Of 296 patients with RCC, 61 met the criteria for ACKD. The most common somatic mutations in the overall cohort were in VHL (126, 42.6%), PBRM1 (102, 34.5%), and SETD2 (54, 18.2%). BAP1 had a decreased mutational frequency in RCC specimens from patients without ACKD as compared to those with ACKD (10.6% versus 1.6%), but this was not statistically significant in univariable (OR 0.14, p = 0.056) or multivariable (OR 0.15, p = 0.067) analysis. Median OS was not reached in either cohort. Conclusions: Using the clinicogenomic ORIEN database, our study found lower rates of BAP1 mutations in RCC specimens from patients with ACKD, which may reflect a BAP1-independent mutational driver of RCC in patients with ACKD.

A Minimalistic 3D Self-Organized UAV Flocking Approach for Desert Exploration

In this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value.

Tomographic Planning of Dental Implants Using a Smartphone: Linear Measurements and Implant Selection-A Cross-Sectional Study.

This study assessed the accuracy of linear measurements and dental implant selection performed in a smartphone application compared to a computer software. Forty-four DICOM files from partially edentulous patients in the posterior region of the mandible were evaluated by three trained and calibrated examiners. Images were analyzed using three visualization methods: cross-sectional view (CScr) and coronal (CSco) plane using the CS 3D Imaging software, and coronal (DRco) plane using the DroidRender smartphone application. For the purpose of standardization, measurements of bone width and height were taken in the edentulous posterior region of the mandible, 10 mm posterior to the mental foramen. Dental implant selection was made according to Straumann® Standard implant catalog 2022/2023. Linear measurements were compared using the ANOVA test for repeated measurements and implant selection was compared using the Friedman test. Statistical analysis was performed using the SPSS software, considering a significance level of 5%. Linear measurements showed a statistically significant difference for bone width (p = 0.030), but not for height (p = 0.685). The choice of implant was not influenced by the visualization method (p = 0.614). It is possible to suggest the use of smartphone applications as an additional diagnostic tool for DICOM evaluation and dental implant planning in the posterior region of the mandible. Smartphone applications allow an assessment not limited to 2D images or to static environments. Therefore, information exchange and diagnostic opinion in emergency situations may be facilitated.

Automatic breast ultrasound (ABUS) tumor segmentation based on global and local feature fusion.

Accurate segmentation of tumor regions in automated breast ultrasound (ABUS) images is of paramount importance in computer-aided diagnosis (CAD) system. However, the inherent diversity of tumors and the imaging interference pose great challenges to ABUS tumor segmentation. In this paper, we propose a global and local feature interaction model combined with graph fusion (GLGM), for 3D ABUS tumor segmentation. In GLGM, we construct a dual branch encoder-decoder, where both local and global features can be extracted. Besides, a global and local feature fusion (GLFF) module is designed, which employs the deepest semantic interaction to facilitate information exchange between local and global features. Additionally, to improve the segmentation performance for small tumors, a graph convolution-based shallow feature fusion module (SFFGC) is designed. It exploits the shallow feature to enhance the feature expression of small tumors in both local and global domains. The proposed method is evaluated on a private ABUS dataset and a public ABUS dataset. For the private ABUS dataset, the small tumors (volume smaller than 1 cm³) account for over 50% of the entire dataset. Experimental results show that the proposed GLGM model outperforms several state-of-the-art segmentation models in 3D ABUS tumor segmentation, particularly in segmenting small tumors.

Responsible coordination of municipal health and care services for individuals with serious mental illness: a participatory qualitative study with service users and professionals

BackgroundCare coordination is crucial to ensure the health of individuals with serious mental illness. The aim of this study was to describe and analyze an inclusive innovation process for coordinating municipal health and care services for individuals with serious mental illness.MethodsWe conducted café dialogues with professionals and service users with serious mental illness. The café dialogues engaged participants in conversation and knowledge exchange about care coordination, adressing topics of efficiency, challenges, and improvement. We used a responsible innovation framework to analyze the innovation process.ResultsResponsible coordination requires promoting service users’ health and ensuring communication and mutual awareness between professionals. Individual-level factors supporting responsible coordination included service users knowing their assigned professionals, personalized healthcare services, and access to meaningful activities. Provider-level factors included effective coordination routines, communication, information exchange, and professional familiarity. Results reflect professionals’ and service users’ perspectives on efficient care coordination, existing challenges, and measures to improve care coordination.ConclusionCafé dialogues are an inclusive, participatory method that can produce insights into the responsible coordination of municipal health and care services for individuals with serious mental illness. The responsible innovation framework is helpful in identifying care coordination challenges and measures for responsible coordination.

INNOVATION AND DIGITAL TRANSFORMATION IN HEALTHCARE. PROCESS OPTIMIZATION OF REHABILITATION ACTIVITIES IN IN REHABILITATION CARDIOLOGY IN SUPPORT OF PATIENT AND TEAM

Abstract Physical activity is a treatment for preventing and treating a wide range of diseases; its prescription must be precise and accurate in order to optimize the effects avoiding at the same time clinical risks due to frequent patients’ frailty.To deploy a “comprehensive digital rehabilitation” means to digitalize the whole process: from clinical and instrumental evaluations to the subsequent coherent prescriptions of the rehabilitation and care–related activities, from the case management planning – described in the individual rehabilitative project – to the individual rehabilitative program – that takes shape in semi–automated scheduling and in digitalized reporting. The deployed software must be easy to use and reliable, and must ensure safety, guaranteeing the exchange of clinical information among the team members. For all these reasons, we have pre–set pathways that, starting from the main and secondary clinical diagnosis (using the ICD classification) and based thereafter on the WHO ICF classification, the rehabilitation processes are governed in a standardized tailored granularity for each single patient by the aid of a computerized expert system. Even if it what just stated appears an oxymoron, thanks to the digital transformation and the adoption of the Internet of Things, it is possible to easily implement a personalized rehabilitation program through a shared and standardized ontology, providing benefits both to the patient and to the rehabilitation team. Based on this, the rehabilitation services that can be provided have been defined, providing not only those provided for by national and regional nomenclators but also those peculiar to the activities in our Institutes. The digital gym goes beyond the concept of equipment in a gym space; the gym space becomes both physical and virtual. The goal is the networking of "basic" and innovative rehabilitation equipment, even placed in different environments, through the philosophy of IoT, that path of technological development whereby, through the Internet network, potentially every object of daily experience acquires its own identity in the digital world. All this will allow for real–time feedback of data and measurements for the development of a personalized digital record. The digital platform, telemedicine and cloud computing, begin to enable an integration of people, equipment and data.

It takes two to Tango - Conceptualizing the “succession dance” in family firms through social exchange theory

We draw on Social Exchange Theory (SET) and show that a “succession dance”—a phase of joint successor and predecessor activity inside the firm—occurs due to prior social exchanges between successor and predecessor. It can be guided by altruism, friendship, or partnership (i.e., a generalized exchange relationship) as well as a professional need for informational and material social exchanges at the firm-level (i.e., a restricted exchange relationship). By utilizing mixed methods encompassing quantitative data on 522 CEO successions in family firms and qualitative data from 34 in-depth interviews, we find support for these coherences. We also discuss how altruistic versus transactional motives for predecessors to remain active inform the burgeoning debate on positive versus negative nature repercussions of prolonged predecessor activity.

Optimizing Production Supply Chain With Markov Jump System for Logistics Collaboration

This study employs a novel Markov jump system model to address complexities and uncertainties in modern logistics management, particularly in supply chain logistics information networks. It introduces dynamic memory to tackle issues in traditional static networks, enabling modeling and control of this intricate system. By assessing decision node importance, a novel strategy optimization method is devised. Through information exchange and decision adjustments among cooperating nodes, the overall decision system performance is enhanced, resulting in a comprehensive logistics information coordination mechanism for production supply chains based on the Markov jump system. The research demonstrates that this approach considers node interactions and information exchange, using dynamic memory to improve system adaptability and robustness, ultimately enhancing overall decision performance and stability. This has practical value for decision support and system optimization in production supply chain logistics information networks, offering fresh insights into Markov jump system control.

When Load is Low, Working Memory is Shielded From Long-Term Memory's Influence.

Previous studies found that episodic long-term memory (eLTM) enhances working memory (WM) performance when both novel and previously learnt word pairs must be retained on a short-term basis. However, there is uncertainty regarding how and when WM draws on eLTM. Three possibilities are (a) that people draw on eLTM only if WM capacity is exceeded; (b) that there is always a contribution of eLTM to WM performance, irrespective of whether prior knowledge is helpful or not; or (c) benefits of prior knowledge are specific to comparisons between conditions which are similarly ambiguous concerning whether LTM may be useful. We built on the assumption that under conditions of a contribution from LTM, these LTM traces of memoranda could benefit or hamper performance in WM tasks depending on the match between the traces stored in LTM and the ones to-be stored in WM in the current trial, yielding proactive facilitation (PF) and proactive interference (PI), respectively. Across four experiments, we familiarized participants with some items before they completed a separate WM task. In accordance with possibility (a) we show that there are indeed conditions in which only WM contributes to performance. Performance deteriorated with the addition of stimuli from eLTM when WM load was low, but not when it was high; and an exchange of information between LTM and WM occurred only when WM capacity was exceeded, with PI and PF effects affecting immediate memory performance in verbal and visual tasks only at higher set sizes.

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

Digital twin (DT) technology provides a path for implementing cyber–physical systems (CPS) and developing smart manufacturing because they are essential tools for monitoring and controlling manufacturing processes. It is considered a vital technology in smart manufacturing and is being widely researched in academia and industry. Furthermore, the combination of DTs and immersive environments has shown great potential for integrating novel capabilities into the new generation of CPS. This research presents an architecture for implementing immersive digital twins under ISO 23247 in flexible manufacturing processes. The proposed system is based on the integration of DT technologies in conjunction with augmented reality (AR) and gesture tracking, and validation was performed in the sorting station of the MPS 500 to increase the interaction and flexibility between physical and virtual environments in real time, thus enhancing the capabilities of the DT. The methodology used for the design and implementation of the DT includes (1) general principles and requirements; (2) models with functional views based on domains and entities; (3) attributes of the observable manufacturing elements; and (4) protocols for the exchange of information between entities. The results show that the integration of these technologies improves the monitoring, control, and simulation capabilities of processes using 3D resources and immersive environments, achieving a higher level of interactivity. In addition, error detection tests were carried out, where a reduction of time was observed in the resolution of errors that may be caused by internal or external disturbances of the process, thus avoiding production delays.

The Reality of Digital Rights Management Technologies: A Systematic Mapping Study

With the proliferation of internet applications and services, human life has become different from what it was before, as the speed and ease of information exchange have increased, alongside the potential number of parties that can exchange information. This also means that the concept of security is no longer limited to the computer that retains the original information but has extended to the security of digital information. Moreover, information security no longer applies only to access to it, but also to what the user can do with that information. Encryption is no longer sufficient, as the user can easily or unintentionally transfer unencrypted information to users who are not authorized to access and view it.

Power-Carbon Information Management System Based on Machine Learning

With the deepening reform of the power market and carbon market, great progress has been made in informatization. Power information may be stored in many scattered places, and it is difficult to share data between different departments or systems. This leads to fragmentation and redundancy of information and makes information exchange difficult. Blockchain can improve the reliability of Power-Carbon Management System (briefly described as PCMS for convenience) data processing. PCMS informatization has become the basis for improving the quality and efficiency of project management and maximizing the environmental and economic benefits of the project. Because the power information management system can effectively control the flow of information and resource allocation. Due to the requirement of low-carbon and stable power production, PCMS attaches great importance to the application and implementation of information in power management, but does not attach enough importance to the informatization of power production management. Therefore, this paper analyzed the current situation, characteristics and existing problems of PCMS through machine learning algorithm, then constructed the design principles, and finally proposed the optimization path of PCMS according to the principles. The information collection ability and system control ability of the optimized PCMS were better than the original PCMS. The information collection ability was 14.2% higher than the original, and the system control ability was 9.8% higher than the original. In general, both blockchain and machine learning can improve the data reliability of PCMS.

Development of state information systems in the field of metrology: main tasks

The use of information systems is an integral part of the activities of all federal executive authorities. At the same time, in the conditions of digital transformation the fulfillment of some functions of federal executive bodies is impossible without interaction of their information systems, which often occurs as a result of information exchange (requests and answers to them). The main directions of development of information interaction of state information systems of federal executive authorities, participating to some extent in ensuring the uniformity of measurements, are given. The approaches to the organization of interaction are described, as well as the tasks that can be solved through such interaction of state information systems. On the example of interaction of the subsystem “ARSHIN” of the Federal State Information System of Rosstandart with the State Information System of housing and communal services the tasks aimed at ensuring the reliability of transferred data contained in these information systems are considered. Transition to machine-readable formats of documents in the field of measurement uniformity assurance is proposed. The conditions necessary to ensure interaction of information systems are described.

Catalyzing Urban Dynamics: Fostering Information Exchange, Encouraging Innovation, and Facilitating Knowledge Creation in the Macau Peninsula

Catalyzing Urban Dynamics: Fostering Information Exchange, Encouraging Innovation, and Facilitating Knowledge Creation in the Macau Peninsula

Patient experiences of interprofessional collaboration and intersectoral communication in rare disease healthcare in Germany – a mixed-methods study

BackgroundRare diseases are often complex, chronic and many of them life-shortening. In Germany, healthcare for rare diseases is organized in expert centers for rare diseases. Most patients additionally have regional general practicioners and specialists for basic medical care. Thus, collaboration and information exchange between sectors is highly relevant. Our study focuses on the patient and caregiver perspective on intersectoral and interdisciplinary care between local healthcare professionals (HCPs) and centers for rare diseases in Germany. The aims were (1) to investigate patients’ and caregivers’ general experience of healthcare, (2) to analyse patients’ and caregivers’ perception of collaboration and cooperation between local healthcare professionals and expert centers for rare diseases and (3) to investigate patients’ and caregivers’ satisfaction with healthcare in the expert centers for rare diseases.ResultsIn total 299 individuals of whom 176 were patients and 123 were caregivers to pediatric patients participated in a survey using a questionnaire comprising several instruments and constructs. Fifty participants were additionally interviewed using a semistructured guideline. Most patients reported to receive written information about their care, have a contact person for medical issues and experienced interdisciplinary exchange within the centers for rare diseases. Patients and caregivers in our sample were mainly satisfied with the healthcare in the centers for rare diseases. The qualitative interviews showed a rather mixed picture including experiences of uncoordinated care, low engagement and communication difficulties between professionals of different sectors. Patients reported several factors that influenced the organization and quality of healthcare e.g. engagement and health literacy in patients or engagement of HCPs.ConclusionsOur findings indicate the high relevance of transferring affected patients to specialized care as fast as possible to provide best medical treatment and increase patient satisfaction. Intersectoral collaboration should exceed written information exchange and should unburden patients of being and feeling responsible for communication between sectors and specialists. Results indicate a lack of inclusion of psychosocial aspects in routine care, which suggests opportunities for necessary improvements.

Validation of a New Patient-Reported Outcome Measure of the Functional Impact of Essential Tremor on Activities of Daily Living.

The Essential Tremor Rating Assessment Scale (TETRAS) is a popular scale for essential tremor (ET), but its activities of daily living (ADL) and performance (P) subscales are based on a structured interview and physical exam. No patient-reported outcome (PRO) scale for ET has been developed according to US regulatory guidelines. Develop and validate a TETRAS PRO subscale. Fourteen items, rated 0-4, were derived from TETRAS ADL and structured cognitive interviews of 18 ET patients. Convergent validity analyses of TETRAS PRO versus TETRAS ADL, TETRAS-P, and the Quality of Life in Essential Tremor Questionnaire (QUEST) were computed for 67 adults with ET or ET plus. Test-retest reliability was computed at intervals of 1 and 30 days. The influence of mood (Hospital Anxiety and Depression Scale, HADS) and coping behaviors (Essen Coping Questionnaire, ECQ) was examined with multiple linear regression. TETRAS PRO was strongly correlated (r > 0.7) with TETRAS ADL, TETRAS-P, and QUEST and exhibited good to excellent reliability (Cronbach alpha 95%CI = 0.853-0.926; 30-day test-retest intraclass correlation 95%CI = 0.814-0.921). The 30-day estimate of minimum detectable change (MDC) was 6.6 (95%CI 5.2-8.0). TETRAS-P (rsemipartial = 0.607), HADS depression (rsemipartial = 0.384), and the coping strategy of information seeking and exchange of experiences (rsemipartial = 0.176) contributed statistically to TETRAS PRO in a multiple linear regression (R2 = 0.67). TETRAS PRO is a valid and reliable scale that is influenced strongly by tremor severity, moderately by mood (depression), and minimally by coping skills. The MDC for TETRAS PRO is probably sufficient to detect clinically important change.