Direction Of Information Transfer Research Articles

Who dominate the information flowing between innovative and traditional financial assets? A multiscale entropy-based approach

We investigate the interactions between innovative financial assets (e.g., FinTech-related stocks, green bonds, and cryptocurrencies) and traditional ones (e.g., global equities, gold, crude oil, U.S. dollar, and government bonds) at short-, medium-, and long-time scales by adopting the multiscale entropy-based approach, through which the dominant influencers in information flowing are identified. Also, we discuss the impact of unprecedented events exemplified by the COVID-19 pandemic. Further, we propose the high-transfer-entropy trading strategy that considers the dominant influencers. The empirical results show that (i) the information exchange is heterogenous at three time scales, with the greatest intensity at short-time scale; (ii) no isolated dominant influencer is discovered, and the traditional assets are stronger influencers in long term while the innovative assets are more influential in short and medium terms; (iii) the COVID-19 pandemic alters the magnitude and direction of information transfer, during which the influence of innovative assets relative to traditional assets enhances evidently; and (iv) the high-transfer-entropy strategy can effectively provide the investor with excess returns.

The Effects of Learning Style-Based Differentiated Instructional Activities on Academic Achievement and Learning Retention in the Social Studies Course

Today, individuals should adapt themselves to the rapidly changing and developing information age and have learning skills. It is only possible by adopting new approaches and strategies instead of traditional teaching methods and techniques. Different learning and teaching approaches are characterized by various methods and designs, such as differentiated instruction. The primary purpose of differentiated instruction is to employ teaching activities depending on students’ learning styles and interests. This study aimed to determine the effect of learning style-based differentiated instructional activities on 4th-grade students’ academic achievement and learning retention in the social studies course. This study draws attention to the differences between differentiated teaching activities based on learning style and traditional teaching practices and proposes practical solutions for effective social studies teaching. In this study used a quasi-experimental model with pretest-posttest control group designs. The study group consisted of 62 4th-grade primary school students. The experimental group completed learning style-based differentiated instructional activities in the study, while traditional teaching methods were used in the control group. The data were collected with an academic achievement test and interview forms developed by the researchers. The quantitative data were analyzed using arithmetic means, frequency, independent group t-test, and dependent group t-test, and the qualitative data obtained from interviews were examined using descriptive and content analysis approaches. A significant difference was found in the experimental group students’ academic achievement in the social studies course, in which learning style-based differentiated instructional activities were applied. There was a significant difference in the retention scores between the experimental group, in which learning style-based differentiated instructional activities were applied, and the control group, in which the traditional teaching methods were preferred. The difference was in favor of the experimental group. Learning style-based differentiated instructional activities allowed students to participate in the activities actively, facilitated classroom management, and offered an entertaining and well-prepared learning atmosphere where individual differences and learning paces are welcomed. The teacher-centered teaching approach based on traditional methods and the direct transfer of information to students should be replaced with student-centered approaches responsive to learning styles, individual differences, interests, and potentials. Social Studies Course Curriculum and educational materials should be enriched with differentiated instructional activities. Social Studies Course Curriculum and materials (e.g., textbooks, lesson plans, and printed materials) should be enriched with differentiated instructional activities.

Plasmodesmata: Channels Under Pressure.

Multicellularity has emerged multiple times in evolution, enabling groups of cells to share a living space and reducing the burden of solitary tasks. While unicellular organisms exhibit individuality and independence, cooperation among cells in multicellular organisms brings specialization and flexibility. However, multicellularity also necessitates intercellular dependence and relies on intercellular communication. In plants, this communication is facilitated by plasmodesmata: intercellular bridges that allow the direct (cytoplasm-to-cytoplasm) transfer of information between cells. Plasmodesmata transport essential molecules that regulate plant growth, development, and stress responses. They are embedded in the extracellular matrix but exhibit flexibility, adapting intercellular flux to meet the plant's needs. In this review, we delve into the formation and functionality of plasmodesmata and examine the capacity of the plant communication network to respond to developmental and environmental cues. We illustrate how environmental pressure shapes cellular interactions and aids the plant in adapting its growth. Expected final online publication date for the Annual Review of Plant Biology, Volume 75 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Persistence of Basal Ganglia Oscillatory Activity During Tremor Attenuation by Movement in Parkinson's Disease Patients.

One of the characteristics of parkinsonian tremor is that its amplitude decreases with movement. Current models suggest an interaction between basal ganglia (BG) and cerebello-thalamo-cortical circuits in parkinsonian tremor pathophysiology. We aimed to correlate central oscillation in the BG with electromyographic activity during re-emergent tremor in order to detect changes in BG oscillatory activity when tremor is attenuated by movement. We performed a prospective, observational study on consecutive parkinsonian patients who underwent deep brain stimulation surgery and presented re-emergent tremor. Coherence analysis between subthalamic nucleus/globus pallidus internus (STN/GPi) tremorous activity measured by microrecording (MER) and electromyogram (EMG) from flexor and extensor wrist muscles during rest, posture, and re-emergent tremor pause was performed during surgery. The statistical significance level of the MER-EMG coherence was determined using surrogate data analysis, and the directionality of information transfer between BG and muscle was performed using entropy transfer analysis. We analyzed 148 MERs with tremor-like activity from 6 patients which were evaluated against the simultaneous EMGs, resulting in 296 correlations. Of these, 26 presented a significant level of coherence at tremor frequency, throughout rest and posture, with a complete EMG stop in between. During the pause, all recordings showed sustained MER peaks at tremor frequency (±1.5 Hz). Information flows preferentially from BG to muscle during rest and posture, with a loss of directionality during the pause. Our results suggest that oscillatory activity in STN/GPi functionally linked to tremor sustains firing frequency during re-emergent tremor pause, thus suggesting no direct role of the BG circuit on tremor attenuation due to voluntary movements. © 2024 International Parkinson and Movement Disorder Society.

Switchable-Encoder-Based Self-Supervised Learning Framework for Monocular Depth and Pose Estimation

Monocular depth prediction research is essential for expanding meaning from 2D to 3D. Recent studies have focused on the application of a newly proposed encoder; however, the development within the self-supervised learning framework remains unexplored, an aspect critical for advancing foundational models of 3D semantic interpretation. Addressing the dynamic nature of encoder-based research, especially in performance evaluations for feature extraction and pre-trained models, this research proposes the switchable encoder learning framework (SELF). SELF enhances versatility by enabling the seamless integration of diverse encoders in a self-supervised learning context for depth prediction. This integration is realized through the direct transfer of feature information from the encoder and by standardizing the input structure of the decoder to accommodate various encoder architectures. Furthermore, the framework is extended and incorporated into an adaptable decoder for depth prediction and camera pose learning, employing standard loss functions. Comparative experiments with previous frameworks using the same encoder reveal that SELF achieves a 7% reduction in parameters while enhancing performance. Remarkably, substituting newly proposed algorithms in place of an encoder improves the outcomes as well as significantly decreases the number of parameters by 23%. The experimental findings highlight the ability of SELF to broaden depth factors, such as depth consistency. This framework facilitates the objective selection of algorithms as a backbone for extended research in monocular depth prediction.

Detection of Mild Cognitive Impairment Using Convergent Cross‐Mapping Based on Resting‐State EEG Signals among Community‐Dwelling African American Participants

AbstractBackgroundAccurate and early detection of mild cognitive impairment (MCI) is critical for interventions to promote cognitive resiliency. EEG‐based detection of MCI is attracting considerable attention from the research community due to its accessibility, affordability and increasing accuracy.MethodOur research is based on resting‐state EEG (64‐channel, eye‐closed). The current dataset includes 90 consensus‐diagnosed, community‐dwelling African Americans (ages 60‐90 years, 51 normal cognition, NC; 39 Mild Cognitive Impairment, MCI) recruited through the Wayne State Institute of Gerontology Healthy Black Elders Center and the Michigan Alzheimer’s Disease Research Center, all with subjective cognitive complaints.From an information‐theoretic perspective, causality measures the effective connectivity or directed information transfer from one brain region to another. Convergent cross‐mapping has attracted considerable attention recently since it was proven to be able to detect causal coupling in both deterministic and random settings. In this research, we propose to develop an EEG‐based MCI detection model by exploiting cross‐mapping causality analysis.We first derived the current source density (CSD) from EEG signals and performed cross‐mapping based causality analysis between the CSD signals of all the possible pairs across the selected EEG regions of interest. We then conducted joint time‐frequency‐spatial analysis on the cross‐mapping causation and selected features which are likely to reflect the differences between NC and MCI. The selected features were then fed into a machine‐learning algorithm for discrimination of NC and MCI.ResultBy tuning the observing window size and the number of features used, we obtained a series of different configurations of the discrimination model between NC and MCI, each with its own estimation accuracy. The overall result is a combined output of the model under different configurations, which improve the reliability and sensitivity of the discrimination model. For the 90‐sample dataset we are working with, the leave‐one‐out cross‐validation accuracy of our model is 97.78%.ConclusionOur analysis indicates that, potentially, EEG‐based cross‐mapping causality analysis may serve as a promising assessment tool for early detection of people at risk of MCI and dementia, even when just relying on resting state EEG.Funding: NSF‐2032709/Li; NIH‐1R21AG046637‐01A1/Kavcic and NIH‐1R01AG054484‐01A1/Kavcic; NIH‐ P30AG05376004/Paulson

Temporal Analysis of Energy Transformation in EU Countries

Due to the environmental policy adopted by the European Union (EU), EU countries are obliged to reduce greenhouse gas emissions. They reduce emissions largely through the energy transformation and switching to renewable energy sources (RES). Therefore, it is important to assess the progress of the energy transformation of individual EU countries. This is related to the aim of the article, which is a temporal analysis of the energy transformation process towards the transition to RES and reducing the use of fossil fuels in energy production. To achieve this goal, a new Temporal/Dynamic Multi-Criteria Decision-Making (T/DMCDM) method called Temporal PROSA was developed, based on the PROMETHEE and PROSA family of methods. The Temporal PROSA method, unlike many other T/DMCDM methods, enables the aggregation of data from many periods into a single final assessment, as well as the direct transfer of information from the examined periods to the overall result. As a result of the research, EU countries that dominated in terms of progress in energy transformation towards RES in the years 2004–2021were identified. Based on the data and methodology used, it was indicated that these countries are primarily Sweden and Portugal, and recently also Denmark and Finland. On the other hand, countries such as Belgium, Bulgaria, Cyprus, Luxembourg, and Poland made the least progress between 2004 and 2021.

Characteristics of multi-channel intermuscular directional coupling based on time-varying partial directional coherence analysis

The human body transmits directional information between muscles during upper limb movements, and this will be particularly evident when the dominant muscle changes during movement transitions. By capturing the electromyography (EMG) signals of wrist flexion and extension continuous transition movements, we investigated the characteristics of multichannel intermuscular directional coupling and directional information transmission, and consequently explored the control mechanism of Central nervous system (CNS) and the coordination mechanism of motor muscles. Multi-channel EMG was collected from 12 healthy subjects under continuous translational movements of wrist flexion and extension, and the time-varying biased directional coherence analysis (TVPDC) model was constructed using partial directional coherence analysis (PDC) frequency domain directionality to study the directional information transfer characteristics in the time–frequency domain, screen closely related muscle pairs and perform directional coupling significance analysis. Palmaris longus (PL) played a dominant role under wrist flexion movements(WF), Extensor Carpi Radialis (ECR) played a dominant role under wrist extension movements(WE), and the remaining muscles responded to them with information and Biceps Brachii (BB) played a responsive role throughout the movement; flexor pairs had the highest positive coupling values in the beta band during Conversion action1 (MC1) and WF phases, and extensor pairs had the highest positive coupling values in the gamma band during Conversion action2(MC2) phase and the highest coupling values in the beta band during WE phase. TVPDC can effectively analyze the multichannel intermuscular directional coupling and information transmission relationship of surface electromyography under wrist flexion and extension transition movements, providing a reference for exploring the control mechanism of CNS and abnormal control mechanism in patients with motor dysfunction in a new perspective.

Psychological and pedagogical problems of using chatGPT in solving physical problems

All innovative ways of learning are aimed at enabling the average student to learn to think like an expert, to use his/her knowledge like an expert. Traditional physical education, like all-natural sciences, involves the transfer of information to students in lectures, and its consolidation in practical and laboratory classes and in the form of independent homework. At the same time, several aspects of learning are distinguished: conceptual understanding, direct transfer of information, knowledge and basic physical laws. A general drawback of traditional concepts is the low digestibility of the material, which is related to the psychological characteristics of a person: 10% are able to formulate the main ideas of the material that was taught 15 minutes after the explanation, if it is new material for them. All modern educational technologies using interactive methods and various pedagogical methods are aimed at changing the student's psychology and are called upon in various ways and trajectories to reach the sixth level in Bloom's taxonomy - the level of creativity, expert, specialist.
 The ability to solve problems in physics is an important element in the system of physical education, because it allows you to achieve a number of goals: students see the practical application of the acquired theoretical knowledge, which makes the learning process more conscious and changes the attitude to learning; contributes to the development of logical thinking, concretization of knowledge, which connects the theoretical lecture material with its practical application. In the process of solving physics problems, a number of personal abilities develop: mental, creative, logical, intelligence, observation, independence and accuracy.
 The integration of artificial intelligence (AI) generative models GPT in solving physical problems has attracted considerable attention this year. This article examines the complex interaction between AI and student decision-making, shedding light on the cognitive and emotional factors that must be considered when using AI to solve physical tasks. In addition, the pedagogical implications of incorporating AI into physics education are explored, emphasizing the importance of maintaining a balanced approach that promotes the development of critical thinking, creativity, and ethical reasoning. By diligently addressing these challenges, we can harness the potential of AI to expand problem-solving capabilities while preserving the undeniable value of human intelligence and expertise in scientific research

Microstates of the cortical brain-heart axis.

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.

A short-term residential load forecasting scheme based on the multiple correlation-temporal graph neural networks

Accurate residential load forecasting (RLF) is of great significance for the decision-making and operation of modern power system. In literature, deep neural network (DNN) based RLF schemes have witnessed great development due to the advantage of automatically extracting features and capturing complex non-linear pattern in the presented data. However, most existed works separately exploit the historical data of a specific residential house to forecast its load. However, the electricity consumption behaviors among residential users are not independent, and implicitly have some correlations, which can be explicitly characterized and exploited to improve the accuracy of RLF forecasting. Inspired by this idea, through exploiting the multiple correlations among households, this paper proposes a novel residential load forecasting framework based on multiple correlation-temporal graph neural networks, RLF-MGNN. Specifically, the novelty of our work includes three aspects. First, multiple graphs are explicitly constructed to represent both linear and nonlinear correlations among temporal load series of households. That is, the synchronization graph is built to describe the degree of linear correlation between two households using Pearson correlation coefficient, which characterizes the similarity of their consumption behaviors, and the causality graph is built to describe their nonlinear correlation using transfer entropy, which characterizes the amount of directional information transfer from one time series to another, and models the mutual influence between households. Second, the multiple correlation-temporal graph convolutional networks (GCNs) are designed to forecast the residential users’ loads. In detail, at each timestep, latent features are first extracted by corresponding GCNs to embed multiple correlations among households, and then are sent to Long Short-Term Memory (LSTM) for further learning the latent temporal features. Finally, thorough experiments on real datasets demonstrate that our proposed RLF-MGNN outperforms the state-of-the-art independent DNN based schemes and other GNN based schemes.

Directional prefrontal-thalamic information flow is selectively required during spatial working memory retrieval.

Spatial working memory is a kind of short-term memory that allows temporarily storing and manipulating spatial information. Evidence suggests that spatial working memory is processed through three distinctive phases: Encoding, maintenance, and retrieval. Though the medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval, how the functional interactions and information transfer between mPFC and MD remains largely unclear. We recorded local field potentials (LFPs) from mPFC and MD while mice performed a spatial working memory task in T-maze. The temporal dynamics of functional interactions and bidirectional information flow between mPFC and MD was quantitatively assessed by using directed transfer function. Our results showed a significantly elevated information flow from mPFC to MD, varied in time and frequency (theta in particular), accompanying successful memory retrieval. Elevated theta information flow, a feature that was absent on error trials, indicates an important role of the directional information transfer from mPFC to MD for memory retrieval.

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer.

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared tohealthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta anddelta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

Effective nursing shift handover in critical care: A concept analysis.

To explore the concept of effective shift handover in critical care settings.Ineffective handover is one of the leading causes of adverse events in healthcare settings. Nursing shift handover in the intensive care unit (ICU) is more complex and detailed than in other in-patient wards due to the complexity of medical issues experienced by ICU patients. It is, therefore, critical that nurses be able to conduct handover effectively. Despite the importance of handover, the definition and attributes of effective handover are not clearly understood.Walker and Avant's eight-step approach to concept analysis.Effective handover in nursing is an interactive process between two nurses, resulting in the direct transfer of adequate patient-specific recent information in a manner that creates a shared understanding of information. Its defining attributes are (1) content-related and (2) process-related attributes. The antecedents of effective handover are organizational support, interpersonal relationship, and work environment. Its consequences include patient and nurse satisfaction, improved care, and continuity of care. Empirical referents include several tools developed to measure some of the effective handover attributes.The definition provides a connotative meaning and is the first step toward concept validation and instrument development to measure effective handover practice in the ICU.

HAS THE COVID-19 PANDEMIC AFFECTED THE FLOW OF INFORMATION AMONG CRYPTOCURRENCIES?

In this paper, we investigated the impact of the COVID-19 pandemic on the cryptocurrency market. The direction of information transfer between the 67 digital cryptocurrency markets was evaluated, in particular Bitcoin, Ethereum, Litecoin, and Ripple, and we determined which of them were the most influential in the markets. The comparison of the first half of 2019 (outside the pandemic of COVID-19) against the first semester of 2020 (during the COVID-19 pandemic) was used to analyze the pandemic influence. We found two distinct behaviors: (i) in 2019, Bitcoin, as the primary capitalization bond, presented a more substantial transfer of information than the other cryptocurrencies toward Bitcoin [Formula: see text] Ripple (0.0541), followed by Litecoin [Formula: see text] Ripple (0.0522); (ii) in 2020, the most substantial transfers of information occurred from Ethereum to other cryptocurrencies (Litecoin, Bitcoin, and Ripple, in that order). In this period, the weakest transfers happened from Litecoin [Formula: see text] Ripple and in the opposite direction, with equal value (0.0104). Our results indicate that there was a change in the direction of the information flow between the investigated cryptocurrencies, where ETH became the dominant cryptocurrency during the period of turbulence caused by the COVID-19 pandemic.

Spatial-temporal evolution of meteorological and groundwater droughts and their relationship in the North China Plain

Understanding the propagation dynamics from meteorological droughts to groundwater droughts and their spatial–temporal evolution is essential for monitoring and assessing groundwater drought risk. In this study, the Standardized Precipitation and Evapotranspiration Index (SPEI) and Gravity Recovery and Climate Experiment (GRACE) Groundwater Drought Index (GDI) were used for assessing meteorological and groundwater droughts, respectively, in the North China Plain (NCP). The Directed Information Transfer Index (DITI) was used to identify the propagation from meteorological droughts to groundwater droughts. The verification results indicated good reliability of GRACE data for assessing drought events in the NCP. From the view of temporal variation, the SPEI-1 was first decreased and then increased, and the minimum value was in 2011. The GDI was first increased and then decreased, and the maximum was in 2008. In terms of spatial distribution, the SPEI-1 significantly decreased in the central part of the NCP before 2011. The 2011–2020 period revealed an increasing trend in SPEI-1 in many parts of the NCP. According to the GDI results, an increasing trend was observed in the central and western parts of the NCP before 2008, and many parts of the study region revealed a decreasing trend in GDI during the 2008–2020 period. The propagation time of SPEI-1 to GDI was long in spring and winter and short in summer. The maximum DITI values ranged from 0.4 to 0.9 and the most spread time was about 18 to 24 months in the northern and southern parts of Hebei and the northern part of Henan province, covering 36.40% of the total surface area of the NCP. Regarding the drought sensitivity and propagation rate, the dominated distribution was in a pattern of high in the northern and low in southern parts of the NCP, with the rates of 38.44% and 34.17%, respectively.

Modeling the phased implementation of headhunting as a way to fill vacancies

The object of research is a set of stages of processes, used in the application of hunting as a method of closing vacancies. Such stages include: sources of search for candidates, ways of their interest formation, telephone conversation as an interview, negotiations and compilation of statistics with direct transfer of information to the director of the company.
 In the course of the study, such general scientific and specific research methods as analysis and synthesis, induction, deduction, as well as methods of comparison, observation and a systematic approach, were used. These methods are to determine the results and dynamics when recruiting strategies are changed or when they are combined. With the help of comparison methods and a systematic approach, it was possible to determine the optimal strategy for closing the required number of vacancies in the future. Using the observation method, it was possible to consider the dynamics of indicators from each selection method separately or in different combinations with each other.
 Among the complex methods, an analysis was used, which allowed to understand the dynamics of indicators and draw conclusions based on them on each of the options for implementing the methods. With the help of induction on the basis of a set of conclusions about each of the options separately, a generalized conclusion was made about the further rationality of the method of hunting as effective for businesses. The simulation allowed us to develop a strategy for the phased implementation of hunting based on direct search and understanding of its difference with the latter. With regard to theoretical methods, in the process of research the transition was made from the definitions and general provisions of the hunt to a specific consideration of the method in the enterprise and its direct implementation.
 The result of all studies was:
 – summary of theoretical aspects of headhunting as an effective method of attracting staff;
 – effective change of dynamics of indicators at the enterprise during introduction of hunting and its combination with direct search;
 – a developed strategy for the phased implementation of the hunt to increase the effectiveness of the method.

Investigating the Propagation From Meteorological to Hydrological Drought by Introducing the Nonlinear Dependence With Directed Information Transfer Index

AbstractMeteorological drought is the fuse of hydrological drought, and understanding the propagation mechanisms from meteorological to hydrological drought is of great significance to the monitoring and prevention of hydrological drought. Besides the linear dependence, this study thoroughly investigated the propagation from meteorological to hydrological drought by introducing the nonlinear dependence with directed information transfer index (DITI) for the first time. In this study, the standardized precipitation index and the standardized runoff index were used to represent meteorological drought and hydrological drought, respectively. A new drought response time (DRT) evaluation system was constructed based on the maximum Pearson correlation coefficient and DITI, simultaneously considering the linear and nonlinear relationships between meteorological drought and hydrological drought. Moreover, the relationships of drought characteristics (duration and severity) between these two types of drought were established by using run theory and mathematical function, and the trigger thresholds from meteorological to hydrological drought were then determined. The results indicate that: (a) the effective drought propagation rate was mainly affected by the characteristics of meteorological drought events and the sensitivity of hydrological drought to meteorological drought; (b) the DRT in the Pearl River Basin (PRB) was mainly concentrated in 2–5 months, and the drought translation rate in the PRB was relatively large; and (c) the duration of hydrological drought events was longer in the sub‐regions with smaller meteorological drought trigger thresholds.

Cellular Crosstalk between Endothelial and Smooth Muscle Cells in Vascular Wall Remodeling.

Pathological vascular wall remodeling refers to the structural and functional changes of the vessel wall that occur in response to injury that eventually leads to cardiovascular disease (CVD). Vessel wall are composed of two major primary cells types, endothelial cells (EC) and vascular smooth muscle cells (VSMCs). The physiological communications between these two cell types (EC–VSMCs) are crucial in the development of the vasculature and in the homeostasis of mature vessels. Moreover, aberrant EC–VSMCs communication has been associated to the promotor of various disease states including vascular wall remodeling. Paracrine regulations by bioactive molecules, communication via direct contact (junctions) or information transfer via extracellular vesicles or extracellular matrix are main crosstalk mechanisms. Identification of the nature of this EC–VSMCs crosstalk may offer strategies to develop new insights for prevention and treatment of disease that curse with vascular remodeling. Here, we will review the molecular mechanisms underlying the interplay between EC and VSMCs. Additionally, we highlight the potential applicable methodologies of the co-culture systems to identify cellular and molecular mechanisms involved in pathological vascular wall remodeling, opening questions about the future research directions.

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed information transfer between EEG signals within δ, θ, α, β and γ-bands. The developed methodology is then used to study the properties of directed BNs in children with attention-deficit hyperactivity disorder (ADHD) and compare them with that of the healthy controls using both statistical and receiver operating characteristic (ROC) analyses. The results indicate that directed information transfer between scalp EEG electrodes in the ADHD subjects differs significantly compared to the healthy ones. The results of the statistical and ROC analyses of frequency-specific graph measures demonstrate their highly discriminative ability between the two groups. Specifically, the graph measures extracted from the estimated directed BNs in the β-band show the highest discrimination between the ADHD and control groups. These findings are in line with the fact that β-band reflects active concentration, motor activity, and anxious mental states. The reported results show that the developed methodology has the capacity to be used for investigating patterns of directed BNs in neuropsychiatric disorders.