Information Exchange Research Articles

The P-type calcium pump Spf1 regulates immune response by maintenance of the endoplasmic reticulum-plasma membrane contacts during Candida albicans systemic infection

ABSTRACT Spf1 is an important P-type ATPase in Candida albicans, which functions as an endoplasmic reticulum calcium pump to maintain calcium homoeostasis. The deficiency of Spf1 attenuates the virulence of C. albicans. However, its impact on immune response remains to be investigated. This study discovered that deletion of SPF1 resulted in a reduction of endoplasmic reticulum-plasma membrane contacts, an important structure mediating material and information exchange. This effect was attributed to the reduced plasma membrane localisation of the crucial endoplasmic reticulum-plasma membrane tethering proteins Ist2 and Tcb1/3. The reduction of the contacts led to a decrease in secretion of the virulence factors phospholipase, secreted aspartyl protease (SAP), candidalysin, and the cell wall-anchored protein Hwp1 during infection. Immunofluorescence staining and quantitative PCR assays further showed that the SPF1 deletion led to a remarkable decrease in the levels of pro-inflammatory cytokines, suggesting the alleviation of the fungus-induced inflammatory response. Ultimately, the regulatory role of Spf1 in immune response significantly weakened the infectivity of C. albicans, and increased the survival rate of the hosts. This finding elucidated the role of fungal calcium pump-governed endoplasmic reticulum-plasma membrane contacts in regulation of immune response. It also makes it possible to regulate the host’s immune response via control of SPF1 expression and functions, providing a theoretical basis for treating fungal infections.

Reducing Measurement Costs by Recycling the Hessian in Adaptive Variational Quantum Algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with ADAPT-VQE, an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Rumor detection model with weighted GraphSAGE focusing on node location

While social media platforms promote people’s information exchange and dissemination, they also make rumors spread rapidly on online platforms. Therefore, how to detect rumors quickly, timely and accurately has become a hot topic for scholars in related fields. Traditional deep learning models ignore the relationship and topology between nodes in the rumor detection task and use fixed weights or mean aggregation strategies in the feature aggregation process, which fail to capture the complex interactions between nodes and the dynamics of information propagation, limiting the accuracy and robustness of the rumor detection model. To address these problems, we propose a location-aware weighted GraphSAGE rumor detection model GSMA. We first introduce an attention mechanism that dynamically assigns different attention weights to different neighboring nodes for different degrees of aggregation, improving GraphSAGE’s strategy of using mean-value aggregation for all neighboring nodes during the aggregation process; second, we introduce a modulated position encoding into the model and encode the position information of nodes into the features to improve the model’s ability to perceive the relative position and order of nodes; finally, the post text sentiment is incorporated into the features to provide additional semantic information for the model as a way to achieve rumor detection in microblogging platforms. Experiments show that the accuracy of the GSMA model on Ma-Weibo and Weibo23 reaches 97.43% and 97.55%, which is an improvement of 1.11% and 0.77% compared to the benchmark GraphSAGE, and all the evaluation metrics are also improved compared to other optimal rumor detection models.

Optimizing Agility in the Pharmaceutical Supply Chain Using Digital Twins to Cope with the Ripple Effect

The pharmaceutical supply chain is a multilayered and complex structure designed to deliver medicines to customers in a timely manner while ensuring optimal quality and quantity of medicines. The COVID-19 outbreak exposed the vulnerability and uncertainty of the pharmaceutical supply chain, so managing the risks in the pharmaceutical supply chain has become particularly important. This study demonstrates that digital twin (DT) technology can improve pharmaceutical supply chain agility and reduce the ripple effect caused by disruptions. The ripple effect refers to the effect that a sudden disruption at one node in the supply chain has on causing a chain reaction in the rest of the supply chain. The most used risk management method today is the Enterprise Resource Planning (ERP) system, which allows for real-time data sharing but has limitations in predicting and modeling the entire supply chain and lacks the ability to make quick decisions and simulate the entire supply chain. DT technology creates a virtual model of the supply chain, which enables continuous communication and information exchange between real assets and the virtual model, offering a promising alternative to risk management for pharmaceutical supply chains. risk management by providing a promising alternative. This study utilizes the AnyLogistix platform to construct a DT model and demonstrates the effectiveness of DT technology in reducing ripple effects and improving supply chain agility using quantitative analysis. This paper focuses on analyzing the pharmaceutical supply chain for 75mg aspirin tablets in London. A supply chain that operates normally, a supply chain that introduces a disruptive event (closure of the logistics center due to an earthquake), and a supply chain that applies a proactive strategy (activation of an alternate logistics center) to mitigate risk are simulated separately. The positive impact of DT technology on the supply chain is evaluated by analyzing the methods of key performance indicators (KPIs) such as inventory level, order fulfillment rate, and delivery time. The experimental results show that DT technology enhances the responsiveness, flexibility, speed, and proactivity of the supply chain, which significantly improves the agility of the supply chain. Proactive and effective strategies also reduce the financial impact of disruptions and service levels are quickly recovered. In addition, the proactive strategy stabilized inventory levels and reduced delayed orders. The above key metrics confirm the hypothesis that DT technology can improve supply chain agility and effectively reduce the ripple effect. Despite the significant conclusions drawn in this paper, there are some limitations. Firstly, sensitivity analysis and t-test were not used in the study for secondary testing, which resulted in the lack of reliability of the validation results. In addition, the possible interaction between the ripple effect and the bullwhip effect was not discussed, leading to a confounding factor in the experimental results. The above issues can be considered in further research in the future. Finally, this paper explores the possibilities and uses of introducing machine learning techniques and considers the supply chain risk factors of globalization. In summary, this study validates the potential of digital twin technology in improving agility and coping with ripple effects in pharmaceutical supply chains. As research continues, DT technology is expected to improve supply chain efficiency and reduce supply chain risk while ensuring patient safety.

Sounds Good? Fast and Secure Contact Exchange in Groups

Trustworthy digital communication requires the secure exchange of contact information, but current approaches lack usability and scalability for larger groups of users. We evaluate the usability of two secure contact exchange systems: the current state of the art, SafeSlinger, and our newly designed protocol, PairSonic, which extends trust from physical encounters to spontaneous online communication. Our lab study (N=45) demonstrates PairSonic's superior usability, automating the tedious verification tasks from previous approaches via an acoustic out-of-band channel. Although participants significantly preferred our system, minimizing user effort surprisingly decreased the perceived security for some users, who associated security with complexity. We discuss user perceptions of the different protocol components and identify remaining usability barriers for CSCW application scenarios.

HOXD9/APOC1 axis promotes macrophage M1 polarization to exacerbate diabetic kidney disease progression through activating NF-κB signaling pathway

BackgroundDiabetic kidney disease (DKD) is a complication caused by end-stage diabetes mellitus and usually results in glomerular podocyte injury. Exosomes are important for intercellular information exchange. However, the effect of podocyte exosomes on DKD has not been elucidated.MethodsGEO, PROMO, and GSE1009 databases were used to identify the gene APOC1 and transcription factor HOXD9. qRT-PCR, western blot, and transmission electron microscopy (TEM) were investigated to confirm APOC1 change in high glucose-treated podocytes and exosomes. Flow cytometry, immunofluorescence, qPCR, immunoblotting, wound healing, Transwell invasion assays, dual luciferase assay, and ChIP-PCR assay were performed to detect the effect of APOC1 and HOXD9 on macrophage polarization in high glucose-treated podocytes and exosomes. qRT-PCR and immunoblotting assays were employed to assess the impact of APOC1 knockdown on the M1 polarization of macrophages in response to liraglutide treatment.ResultsThe results suggested that the expression of APOC1 in human podocytes (HPC) and exosomes was elevated. High glucose-treated HPC exosomes promoted macrophage M1-type polarization, which was reversed by adding sh-APOC1. Afterward, HOXD9 was identified as a potential transcription factor for APOC1. Knockdown of HOXD9 led to macrophage M2 polarization, and overexpression of APOC1 polarized macrophage M1. In addition, enhanced p65 phosphorylation verified that HOXD9/APOC1 induced macrophage M1-type polarization by activating the NF-κB signaling pathway. Knocking down APOC1 enhanced the inhibitory effect of liraglutide on macrophage M1 polarization.ConclusionOur findings highlighted that HOXD9/APOC1 was a key player in causing podocyte injury in diabetic kidney disease and led to macrophage M1 polarization through the NF-κB signaling pathway.

Physician perspectives on communication quality in pediatric care.

We aimed to explore physician perspectives on communication quality across pediatric contexts. We conducted semi-structured interviews over a 4-month period. Purposive sampling was conducted to ensure a broad sampling of pediatricians from multiple subspecialties and practice settings. Interviews were conducted until thematic saturation was reached. An interview guide was created based on existing literature. Interviews were transcribed and analyzed for key themes. Eleven pediatricians enrolled in our study. Following thematic analysis, results were organized into four primary themes: Communication Education, High Quality Communication; Low Quality Communication; and Communication Factors and Barriers. Participants prioritized information transfer as a key aspect of communication quality. They identified communication quality as having wellness implications for clinicians. Participants prioritize information transfer and state that clinicians often neglect bidirectional information exchange. Participants often lacked formal communication skills training which may make it difficult to navigate common barriers such as language and cultural differences, lack of time, and interprofessional dysfunction. Formal communication skills training may help overcome challenges and may enhance physician wellness while improving patient care. While the importance of clinician-parent communication in pediatric settings has been established, little is known about provider perceptions of communication quality. Participants identified information transfer as a key aspect of communication quality, often neglecting bidirectional information exchange. Participants often lacked formal communication skills training which may make it difficult to navigate common barriers such as language and cultural differences, lack of time, and interprofessional dysfunction. Formal communication skills training may help with these challenges and may enhance physician wellness while improving patient care. More work is needed to explore the impact of communication skills training on each of these factors.

Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence

Background Early warning systems (EWSs) are tools that integrate clinical observations to identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and appropriate interventions. EWSs can mitigate the impact of global infectious diseases by enhancing information exchange, monitoring, and early detection. Objective We aimed to evaluate the effectiveness of EWSs in acute respiratory infections (ARIs) through a scoping review of EWSs developed, described, and implemented for detecting novel, exotic, and re-emerging ARIs. Methods We searched Ovid MEDLINE ALL, Embase, Cochrane Library (Wiley), and CINAHL (Ebsco). The search was conducted on October 03, 2023. Studies that implemented EWSs for the detection of acute respiratory illnesses were included. Covidence was used for citation management, and a modified Critical Appraisal Skills Programme (CASP) checklist was used for quality assessment. Results From 5838 initial articles, 29 met the inclusion criteria for this review. Twelve studies evaluated the use of EWSs within community settings, ranging from rural community reporting networks to urban online participatory surveillance platforms. Five studies focused on EWSs that used data from hospitalization and emergency department visits. These systems leveraged clinical and admission data to effectively detect and manage local outbreaks of respiratory infections. Two studies focused on the effectiveness of existing surveillance systems, assessing their adaptability and responsiveness to emerging threats and how they could be improved based on past performance. Four studies highlighted the integration of machine learning models to improve the predictive accuracy of EWSs. Three studies explored the applications of national EWSs in different health care settings and emphasized their potential in predicting clinical deterioration and facilitating early intervention. Lastly, 3 studies addressed the use of surveillance systems in aged-care facilities, highlighting the unique challenges and needs of monitoring and responding to health threats in environments housing vulnerable populations. The CASP tool revealed that most studies were relevant, reliable, and of high value (score 6: 11/29, 38%; score 5: 9/29, 31%). The common limitations included result generalizability, selection bias, and small sample size for model validation. Conclusions This scoping review confirms the critical role of EWSs in enhancing public health responses to respiratory infections. Although the effectiveness of these systems is evident, challenges related to generalizability and varying methodologies suggest a need for continued innovation and standardization in EWS development.

During haptic communication, the central nervous system compensates distinctly for delay and noise.

Physically connected humans have been shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is important to understand the impact that network features such as lag and noise may have on human behaviour. In this paper, we investigated interaction with a human-like robot controller that provides similar haptic communication behaviour as human-human interaction and examined the influence and compensation mechanisms for delay and noise on haptic communication. The results of our experiments show that participants can perceive a difference between noise and delay, and make use of compensation mechanisms to preserve performance in both cases. However, while noise is compensated for by increasing co-contraction, delay compensation could not be explained by this strategy. Instead, computational modelling suggested that a distinct mechanism is used to compensate for the delay and yield an efficient haptic communication.

Path analysis of social network factors influencing sustainable eco-city development in the northeast of Thailand

In Urban development, diversity respect is needed to prioritize and balance the urban development design for sustainable eco-city development. As a result, this research aimed to investigate the causal factor pathways of social network factors influencing sustainable eco-city development in the northeastern region of Thailand through a quantitative research approach. With the aim to survey insightful information, the analysis unit was conducted at the individual level with three hundred and eighty-three (383) samplings in Khon Kaen and Udon Thani provinces, including univariate analysis and multivariate analysis, using path analysis and multiple linear regression. The study results indicated that two pathways of social network factors influencing sustainable eco-city development were indirect influence factors. The indirect influence factor consists of information exchange, benefits exchange in the network, and members’ role in the social network. Additionally, the study revealed that the pathway has influences through social network types and the economic and social dimensions of sustainable cities (R2 = 0.330). Therefore, this study concluded that sustainable eco-city development should be implemented through community networks and economic and social network development for environmental development through social network types.

Repeatability of 3D MR fingerprinting during scanner software upgrades.

This study aims to quantify the repeatability of a 3D Magnetic Resonance Fingerprinting (MRF) research protocol in the context of a scanner software upgrade. All of MRI assumes consistent hardware performance and raw data pre-processing on the acquisition side. Software upgrades can affect hardware specifications and reconstruction chain parameters. Understanding how vendor-provided software upgrades vary MRF-derived T1 and T2 values is crucial for its application in different settings. Eight healthy volunteers were imaged with an in-house developed 3D MRF pulse sequence using a 3T scanner before and after a software upgrade (VA31A to VA50A, MAGNETOM Vida, Siemens Healthineers). Online MRF reconstruction using Singular Value Decomposition (SVD) timeseries compression and B1+ correction was performed. The study involved test-retest repeatability assessment and a comparison of pre- and post-upgrade data based on automatically extracted T1 and T2 values from MNI-152 Harvard-Oxford Subcortical Structural Atlas regions. Significant mismatches were found directly after the upgrade. However, after an information exchange with the vendor, the 3D-MRF sequence showed consistent repeatability in both intra-version test-retest scenarios and cross-version comparisons: 1.16 ± 3.18% variability in T1 and 0.54 ± 4.84% in T2 for intra-version tests, and 0.83 ± 3.68% (T1) and 0.05 ± 5.81% (T2) variability for cross-version comparisons. The study shows the reliable performance of 3D MRF protocols across software upgrades is possible, but it also highlights the importance of detailed evaluation and vendor collaboration in ensuring consistency. These findings support the application of MRF in longitudinal studies and emphasize the need for systematic assessments following hardware or software modifications.

Improving communication between volcano observatories and volcanic ash advisory centres in Europe—outcomes from a first workshop

AbstractEffective aviation response to atmospheric emissions from volcanic eruptions relies upon timely communication and information exchange. To explore the current and best practices in this area, a workshop was held in February 2019 under the framework of the European H2020 EUROVOLC project (2018–2021). The objective of the workshop was to bring together the volcano observatories (VOs), volcanic ash advisory centres (VAACs), and volcanological research institutes in Europe. The workshop included scientists and operational staff from six of Europe’s VOs (Iceland, Italy, France, Spain, Portugal, and Greece), the London and Toulouse VAACs, and seven supporting institutions and organisations. During the workshop, participants identified future improvements to the way the VOs and VAACs communicate and share information. Recommendations include refining the guidance and local documentation on the correct use of the Volcano Observatory Notice for Aviation (VONA), improving the two-way feedback between the VAAC and VOs, regular sharing of volcano status updates by the VO, and inclusion of all VOs in regular VAAC and aviation exercises. These are applicable to VOs and VAACs elsewhere in the world.

STRUCTURAL AND FUNCTIONAL ASPECTS OF POLITICAL COMMUNICATION IN THE CONTEXT OF MODERN SOCIAL DEVELOPMENT

The article highlights the structural and functional features of political communication as a process of communicative interaction in the political sphere in the conditions of modern social development. It was determined that political communication is a communicative process of mutual exchange of political information and broadcasting of political discourse among political subjects, state administration bodies, civil society, aimed at achieving consensus in making political and managerial decisions, legitimizing power and maintaining the stability of the democratic political system through means mass communication, Internet social networks, informal contacts, etc. The leading functions of political communication in terms of its structural elements are distinguished: 1) subjects of political communication: citizens, state authorities and management, political parties, movements and associations, civil society institutions, associations and groups of users of social networks on the Internet etc; 2) feedback between the subjects of political communication, acting as a communicative interaction that takes on different forms of information exchange; 3) information message as a relevant political discourse; 4) channels and means of information transmission and two-way communication between subjects of political communication.

The Beauty and Importance of Scientific Journals

For more than 300 years, academic journals have been the main means of communication in all disciplines to disseminate scientific discoveries and advances, both theoretical and methodological, through scientific article, since they have covered all the functions of knowledge generation. Writing articles is the highlight of the research process and through it we not only share ideas and foster the spirit of free and cooperative exchange of information, but also fuel the process of self-correction that makes science powerful. To be shared with the community, articles must go through a process of attribution of authorship, certification of validity, dissemination, distribution and archiving of knowledge. Throughout history, scientific journals have gone through different stages and through different problems inherent to the editorial process. In this essay on scientific publications, we will reflect on the nature of publications, their importance and usefulness both social and economic, their less luminous side as well as their role, as a systemic and almost irreplaceable mechanism, in the generation and transmission of scientific culture, of our knowledge and understanding about the world and about ourselves.

The role of λαϊκή αγορά (“street market”) in modern greek towns

The article discusses the phenomenon of “people’s market” in contemporary Greek cities and towns. Historically, the market in Greece has played an important role not only as a place of trade, but also as a concentration of social and political life. The word αγορά, derived from the ancient Greek verb ἀγορεύω (“to speak publicly”), meant not only a market square but also people’s assembly in Ancient Greece. In modern society, the “people’s market” has almost lost its political function. From a commercial point of view, it should have been replaced by mega-stores, supermarkets, and online commerce, especially during the covid pandemic. However, today in Greece this phenomenon does not lose its relevance and even acquires new functions. In a large city, the “people’s market” is embedded in a new ideology of environmental friendliness of the goods for sale and in the discourse related to the identity of the Modern Greek. In addition, a street market in a city retains the archaic functions of socializing residents, exchanging information, and being a barometer of political sentiment. For rural residents, λαϊκή αγορά is an opportunity to sell an agricultural product and exchange information and contact with the urban environment. Internationally, outside Greece, the Greek folk market is sometimes positioned as a distinct brand.

A new approach for spatio-temporal interface treatment in fluid–solid interaction using artificial neural networks employing coupled partitioned fluid–solid solvers

Partitioned fluid–solid interaction (FSI) problems involving non-conforming grids pose formidable challenge in interface treatment, especially for information exchange, interface tracking, and field variable interpolation between solvers in both space and time. These demand special considerations for accurate and efficient simulations. This paper presents an application of artificial neural networks (ANN) for the interface treatment in a coupled FSI problem employing partitioned solvers. A shallow time-series ANN (nonlinear auto-regressive model with exogenous inputs, NARX) scheme is proposed to handle the exchange of Neumann/Dirichlet information at the coupling interface. This scheme involves two interface treatment models that were developed and analysed. The proposed models interpolate and transfer loads from the fluid to the solid domains, and conversely, displacements from the solid to the fluid domains between non-collocated grids. To validate this approach, we tested it on a 3D FSI problem, which involved damped oscillations of a flexible flap submerged in a fluid cavity. Adequately trained NARX interface models demonstrate reliable input–output mapping and accurate prediction of transient behaviour at the interface. Additionally, we explored the concept of reduced-order modelling (ROM) in the time domain. This allowed us to reduce the model’s complexity by half. Different training algorithms were evaluated to enhance the efficiency and performance of the proposed scheme. The study demonstrates that NARX networks trained with Bayesian Regularization (BR) and Levenberg–Marquardt (LM) algorithms exhibit the best accuracy, while the scaled conjugate gradient (SCG)-based training method provides better computational efficiency with acceptable accuracy. Overall, the NARX interface models provide precise performance and offer a viable potential for applications in FSI problems requiring accurate and faster computations.

JamBIT: RL-based framework for disrupting adversarial information in battlefields

During battlefield operations, military radios (hereafter nodes) exchange information among various units using a mobile ad-hoc network (MANET) due to its infrastructure-less and self-healing capabilities. Adversarial cyberwarfare plays a crucial role in modern combat by disrupting communication between critical nodes (i.e., nodes mainly responsible for propagating important information) to gain dominance over the opposing side. However, determining critical nodes within a complex network is an NP-hard problem. This paper formulates a mathematical model to identify important links and their connected nodes, and presents JamBIT, a reinforcement learning-based framework with an encoder–decoder architecture, for efficiently detecting and jamming critical nodes. The encoder transforms network structures into embedding vectors, while the decoder assigns a score to the embedding vector with the highest reward. Our framework is trained and tested on custom-built MANET topologies using the Named Data Networking (NDN) protocol. JamBIT has been evaluated across various scales and weighting methods for both connected node and network dismantling problems. Our proposed method outperformed existing RL-based baselines, with a 24% performance gain for smaller topologies (50-100 nodes) and 8% for larger ones (400-500 nodes) in connected node problems, and a 7% gain for smaller topologies and 15% for larger ones in network dismantling problems.

Bayesian deep learning: An enhanced AI framework for legal reasoning alignment

The integration of artificial intelligence into the field of law has penetrated the underlying logic of legal operations. Currently, legal AI systems face difficulties in representing legal knowledge, exhibit insufficient legal reasoning capabilities, have poor explainability, and are inefficient in handling causal inference and uncertainty. In legal practice, various legal reasoning methods (deductive reasoning, inductive reasoning, abductive reasoning, etc.) are often intertwined and used comprehensively. However, the reasoning modes employed by current legal AI systems are inadequate. Identifying AI models that are more suitable for legal reasoning is crucial for advancing the development of legal AI systems.Distinguished from the current high-profile large language models, we believe that Bayesian reasoning is highly compatible with legal reasoning, as it can perferm abductive reasoning, excel at causal inference, and admits the "defeasibility" of reasoning conclusions, which is consistent with the cognitive development pattern of legal professionals from apriori to posteriori. AI models based on Bayesian methods can also become the main technological support for legal AI systems. Bayesian neural networks have advantages in uncertainty modeling, avoiding overfitting, and explainability. Legal AI systems based on Bayesian deep learning frameworks can combine the advantages of deep learning and probabilistic graphical models, facilitating the exchange and supplementation of information between perception tasks and reasoning tasks. In this paper, we take perpetrator prediction systems and legal judegment prediction systems as examples to discuss the construction and basic operation modes of the Bayesian deep learning framework. Bayesian deep learning can enhance reasoning ability, improve the explainability of models, and make the reasoning process more transparent and visualizable. Furthermore, Bayesian deep learning framework is well-suited for human-machine collaborative tasks, enabling the complementary strengths of humans and machines.

Overall survival and mutational landscape comparison between Appalachian and non-Appalachian patients with ovarian cancer: An Oncology Research Information Exchange Network (ORIEN) analysis

Overall survival and mutational landscape comparison between Appalachian and non-Appalachian patients with ovarian cancer: An Oncology Research Information Exchange Network (ORIEN) analysis

Altered resting-state functional connectivity in a thalamo-cortico-cerebellar network in patients with schizophrenia

The diagnosis of schizophrenia is associated with a complex psychopathology related to disrupted brain circuitry causing a failure in coordinating information across brain sites with no consensus regarding the mechanisms. Although schizophrenia is well-studied, the great majority of studies investigated pre-selected ROIs or Seed-based connectivity. Whole brain ROI-wise studies that consider all ROIs available simultaneously are lacking. This technique helps understand large- and local-scale dynamics of information exchange across the whole brain. We investigated ROI-wise whole brain networks in 35 participants diagnosed with schizophrenia and 41 control participants. To unveil dysfunctions in brain subnetworks and to characterize network topology, we applied a statistical tool specially developed for network comparison called network-based statistic and graph theory. We observed a hyperconnected thalamo-cortico-cerebellar subnetwork in participants with schizophrenia; nodal analysis revealed higher number of thalamic connections. Our results suggest disruptions at the local level of the subnetwork rather than globally spread across the brain and driven by hyperconnectivity. Importantly, this subnetwork emerged from an exploratory analysis directly comparing ROI-wise whole brain network. This fact makes it an important contribution to the field as additional evidence, demonstrating the high reliability of malfunction in the local thalamo-cortico-cerebellar network.