Wide Network Research Articles

Prism adaptation in patients with unilateral lesion of the parietal or cerebellar cortex: A pilot study on two single cases using a concurrent exposure procedure

Neuroimaging studies showed that prism adaptation (PA), a widely used tool for the rehabilitation of neglect, involves a wide network of brain regions including the parietal cortex and the cerebellum. In particular, the parietal cortex has been suggested to mediate the initial stage of PA through conscious compensatory mechanisms as a reaction to the deviation induced by PA. The cerebellum, on the other side, intervenes in sensory errors prediction to update internal models in later stages. It has been suggested that two mechanisms may underlie PA effects: recalibration, a strategic cognitive process occurring in the initial stages of PA, and realignment, a fully automatic reorganization of spatial maps emerging later and more slowly in time. The parietal lobe has been proposed to be involved mainly in the recalibration whereas the realignment would be carried over by the cerebellum. Previous studies have investigated the effects of a lesion involving either the cerebellum or the parietal lobe in PA taking into account both realignment and recalibration processes. Conversely, no studies have compared the performance of a patient with a cerebellar lesion to that of a patient with a parietal lesion. In the present study, we used a recently developed technique for digital PA to test for differences in visuomotor learning after a single session of PA in a patient with parietal and a patient with cerebellar lesions, respectively. The PA procedure, in this case, includes a digital pointing task based on a concurrent exposure technique, which allows patients to fully see their arm during the pointing task. This procedure has been shown to be as effective as the terminal exposure condition in neglect rehabilitation albeit different processes take place during concurrent exposure condition compared to the most used terminal exposure (allowing to see only the final part of the movement). Patients’ performances were compared to that of a control group. A single session of PA was administered to 1) a patient (BC) with left parieto-occipital lesion involving superior parietal lobe (SPL) and inferior parietal lobe (IPL), 2) a patient (TGM) with a stroke in the territory sub-served by the superior cerebellar artery (SCA) , and 3) 14 healthy controls (HC).The task included three conditions: before wearing prismatic goggles (pre-exposure), while wearing prisms (exposure) and after removing the goggles (post-exposure). Mean deviations were calculated for the following phases: pre-exposure, early-exposure, late-exposure, post-exposure. The presence of after-effect was calculated as the difference between pre-exposure and post-exposure conditions. For each of these conditions, patients’ performance was compared to that of the control group by using a modified Crawford t-test. We found that the patient with the parietal lesion had a significantly different performance in the late-exposure and in the post-exposure compared to both HC and the patient with the cerebellar lesion. Conversely, no differences were observed between TGM and HC across all the conditions.Our results show an increase in the magnitude of the adaptation during the late stage of PA in the patient with the parietal lesion whereas no differences in the performance between the cerebellar patient and the controls were found. These results confirm previous studies suggesting that the parietal cortex is an important node of a wider network involved in PA effect. Furthermore, results concerning the cerebellar patient suggest that visuomotor learning is not affected by lesions of the SCA territory when a concurrent exposure is used as, in such case, it less relies on sensory errors prediction to update internal models. Results are discussed considering the novelty of the applied PA technique.

Wide and deep neural networks achieve consistency for classification

While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standard procedures are consistent for classification, i.e., whether such models minimize the probability of misclassification for arbitrary data distributions. In this work, we identify and construct an explicit set of neural network classifiers that are consistent. Since effective neural networks in practice are typically both wide and deep, we analyze infinitely wide networks that are also infinitely deep. In particular, using the recent connection between infinitely wide neural networks and neural tangent kernels, we provide explicit activation functions that can be used to construct networks that achieve consistency. Interestingly, these activation functions are simple and easy to implement, yet differ from commonly used activations such as ReLU or sigmoid. More generally, we create a taxonomy of infinitely wide and deep networks and show that these models implement one of three well-known classifiers depending on the activation function used: 1) 1-nearest neighbor (model predictions are given by the label of the nearest training example); 2) majority vote (model predictions are given by the label of the class with the greatest representation in the training set); or 3) singular kernel classifiers (a set of classifiers containing those that achieve consistency). Our results highlight the benefit of using deep networks for classification tasks, in contrast to regression tasks, where excessive depth is harmful.

Sharing and Co-Generating Knowledges: Reflections on Experiences with PRA and CLTS

The evolution and spread of PRA (Participatory Rural Appraisal or Participatory Reflection and Action) and CLTS (Community‐Led Total Sanitation) have involved activities of sharing and co‐generating knowledge which can loosely be considered a form of Action Learning. Key activities for this have been sequences of participatory workshops which have evolved as creative collective experiences fed by and feeding into wider networking and dissemination. These workshops have been occasions for sharing practice and collating experiences, and going beyond these to generate ideas and evolve and agree principles and good practices. Critical reflections concern power, planning and process, theory of change and impact, lessons learnt, and an ongoing learning process.

The Association of a Geographically Wide Social Media Network on Depression: County-Level Ecological Analysis.

Social connectedness decreases human mortality, improves cancer survival, cardiovascular health, and body mass, results in better-controlled glucose levels, and strengthens mental health. However, few public health studies have leveraged large social media data sets to classify user network structure and geographic reach rather than the sole use of social media platforms. The objective of this study was to determine the association between population-level digital social connectedness and reach and depression in the population across geographies of the United States. Our study used an ecological assessment of aggregated, cross-sectional population measures of social connectedness, and self-reported depression across all counties in the United States. This study included all 3142 counties in the contiguous United States. We used measures obtained between 2018 and 2020 for adult residents in the study area. The study's main exposure of interest is the Social Connectedness Index (SCI), a pair-wise composite index describing the "strength of connectedness between 2 geographic areas as represented by Facebook friendship ties." This measure describes the density and geographical reach of average county residents' social network using Facebook friendships and can differentiate between local and long-distance Facebook connections. The study's outcome of interest is self-reported depressive disorder as published by the Centers for Disease Control and Prevention. On average, 21% (21/100) of all adult residents in the United States reported a depressive disorder. Depression frequency was the lowest for counties in the Northeast (18.6%) and was highest for southern counties (22.4%). Social networks in northeastern counties involved moderately local connections (SCI 5-10 the 20th percentile for n=70, 36% of counties), whereas social networks in Midwest, southern, and western counties contained mostly local connections (SCI 1-2 the 20th percentile for n=598, 56.7%, n=401, 28.2%, and n=159, 38.4%, respectively). As the quantity and distance that social connections span (ie, SCI) increased, the prevalence of depressive disorders decreased by 0.3% (SE 0.1%) per rank. Social connectedness and depression showed, after adjusting for confounding factors such as income, education, cohabitation, natural resources, employment categories, accessibility, and urbanicity, that a greater social connectedness score is associated with a decreased prevalence of depression.

Systematic Literature Review (SLR) and Network Meta-Analysis (NMA) of First-Line Therapies (1L) for Locally Advanced/Metastatic Urothelial Carcinoma (la/mUC).

To compare efficacy outcomes for all approved and investigational first-line (1L) treatment regimens for locally advanced or metastatic urothelial carcinoma (la/mUC) with standard of care (SOC), a network meta-analysis (NMA) was conducted. A systematic literature review (SLR) identified phase 2 and 3 randomized trials investigating 1L treatment regimens in la/mUC published January 2001-September 2021. Three networks were formed based on cisplatin (cis) eligibility: cis-eligible/mixed (cis-eligible patients and mixed populations of cis-eligible/ineligible patients), cis-ineligible (strict; exclusively cis-ineligible patients), and cis-ineligible (wide; including studies with investigator's choice of carbo). Analyses examined comparative efficacy by hazard ratio (HR) for overall survival (OS), and progression-free survival (PFS), and odds ratio (OR) for overall response rate (ORR), with 1L regimens vs. SOC. SOC was gemcitabine + cis (GemCis) or carboplatin (GemCarbo), cis-eligible/mixed network, and GemCarbo cis-ineligible networks. Of 1906 SLR identified citations, 55 trials were selected for data extraction. The NMA comprised 11, 6, and 8 studies in the cis-eligible/mixed, cis-ineligible (strict), cis-ineligible (wide) networks, respectively. In a meta-analysis of SOC control arms, median (95% CI) overall survival (OS) in months varied by network: 13.19 (12.43, 13.95) cis-eligible/mixed, 11.96 (10.43, 13.48) cis-ineligible (wide), and 9.74 (6.71, 12.76) cis-ineligible (strict). Most differences in OS, PFS, and ORR with treatment regimens across treatment networks were not statistically significant compared with SOC. Outcomes with current 1L regimens remain poor, and few significant improvements over SOC have been made, despite inclusion of recent clinical trial data, highlighting an unmet need in the la/mUC patient population.

Active aging model: Sample of Ankara

ABSTRACT Demographic change in Turkey is quite rapid as in other developing countries. The elderly population, which has reached 9.7%, has brought along the problems that need to be solved for our country. It is important to investigate the concept of active aging which is included in the solution proposals. The aim of this research is to develop a Model of Some of the Personal and Social Factors Affecting Active Aging, by emphasizing the personal and social determinants that affect active aging. 280 older adults aged 65 and over, living in Ankara and from different socio-economic levels, participated in the research. Active aging scale, psychological resilience scale, social network scale, and loneliness scale were used as a measurement tool. When the results are examined, it is seen that the path model generally fits well. According to the Path model, we can say that as psychological resilience decreases, loneliness increases, loneliness also weakens the social network and the active aging level of the older adults decreases. On the contrary, we can say that the wide social network increases the active aging level of the older adults. Increasing and realizing social cohesion, social participation, and volunteering opportunities for healthy and active aging in Turkey will enable the expansion of the social networks of older adults and the prevention of loneliness in old age. The obtained implications were discussed within the framework of the studies in the literature, and suggestions were made to individuals, the related institutions, and organizations.

Predicting Heart Disease Based on Wide and Deep Neural Network

Nowadays, more and more people suffer from heart disease because of stressful life, irregular diet, lack of exercise and other reasons. The population affected by heart disease is also younger than ever. If heart disease can be diagnosed as early as possible, it will be of great help in the treatment of heart disease. Thus, this paper proposes models to predict heart disease based on wide and deep neural network, and the result shows that the current work has maintain good performance. Analysis is also provided in this paper to state factors that can affect performance.

Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid’s stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers’ privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers’ activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection.

Unmet Parenthood Goals, Health-Related Quality of Life and Apparent Irrationality: Understanding the Value of Treatments for Infertility.

An increasing number of prospective parents are experiencing infertility along with associated negative impacts on mental health and life satisfaction that can extend across a network of individuals and family members. Assistive reproductive technologies (ART) can help prospective parents achieve their parenthood goals but, like any health technology, they must demonstrate acceptable 'value for money' to qualify for public funding. We argue that current approaches to understanding the value of ART, including quality-adjusted life-year (QALY) gains based on changes in health-related quality of life (HRQOL) and, more often, cost per live birth, are too narrow to capture the full impact of unmet parenthood goals and ART. We see a fundamental disconnect between measures of HRQOL and broader measures of wellbeing associated with met and unmet parenthood goals. We also suggest that simple concepts such as 'patient' and 'carer' are of limited applicability in the context of ART, where 'spillovers' extend across a wide network of individuals, and the person receiving treatment is often not the infertile individual. Consideration of individual and societal wellbeing beyond HRQOL is necessary to understand the full range of negative impacts associated with unmet parenthood goals and the corresponding positive impacts of successful ART. We suggest moving towards a wellbeing perspective on value to achieve a fuller understanding of value and promote cross-sector allocative efficiency.

Wayfinding and path integration deficits detected using a virtual reality mobile app in patients with traumatic brain injury.

The ability to navigate is supported by a wide network of brain areas which are particularly vulnerable to disruption brain injury, including traumatic brain injury (TBI). Wayfinding and the ability to orient back to the direction you have recently come (path integration) may likely be impacted in daily life but have so far not been tested with patients with TBI. Here, we assessed spatial navigation in thirty-eight participants, fifteen of whom had a history of TBI, and twenty-three control participants. Self-estimated spatial navigation ability was assessed using the Santa Barbara Sense of Direction (SBSOD) scale. No significant difference between TBI patients and a control group was identified. Rather, results indicated that both participant groups demonstrated 'good' self-inferred spatial navigational ability on the SBSOD scale. Objective navigation ability was tested via the virtual mobile app test Sea Hero Quest (SHQ), which has been shown to predict real-world navigation difficulties and assesses (a) wayfinding across several environments and (b) path integration. Compared to a sub-sample of 13 control participants, a matched subsample of 10 TBI patients demonstrated generally poorer performance on all wayfinding environments tested. Further analysis revealed that TBI participants consistently spent a shorter duration viewing a map prior to navigating to goals. Patients showed mixed performance on the path integration task, with poor performance evident when proximal cues were absent. Our results provide preliminary evidence that TBI impacts both wayfinding and, to some extent, path integration. The findings suggest long-lasting clinical difficulties experienced in TBI patients affect both wayfinding and to some degree path integration ability.

Analyzing of salient features and classification of wine type based on quality through various neural network and support vector machine classifiers

Wine quality certification is crucial to the wine industry. Indian wine’s superior quality is well-known around the world. Wine quality certification is crucial to the wine industry. Our main objective in this study is to find out a machine-learning model based on experimental data that has been gathered from various places across India and available synthetic data in order to predict wine quality. We utilized 178 wine samples with 13 different physiochemical characteristics. All the features have been analyzed and shown the values of max, min, mean, Kurt, skewness, and standard deviation of each variable. Important attributes that can be selected by comparing the values of all the above-mentioned feature selection approaches were required very much to improve wine quality. Five neural network methods and six support vector methods were trained and tested on all features of the dataset. Narrow neural network, Wide neural network, Quadratic support vector machine and Medium Gaussian support vector machine — all these classifiers showed 97.8% accuracy when trained and evaluated with all features but Quadratic support vector machine achieved this accuracy with the lowest training time 0.92556 sec ad the highest prediction speed 6400 obs/sec. These results demonstrate that the anticipated and experimental responses are extremely well aligned, according to this accuracy and the preferred model is appropriate to classify wine quality based on physiochemical components.

Abstract P2-16-09: Dual Specific Phosphatase-7 (DUSP7/PYST2) and its role in regulating vascular endothelial functions

Abstract Dual Specific Phosphatase-7 (DUSP7/PYST2) and its role in regulating vascular endothelial functions Wenxiao Ji, Wen G. Jiang, Lin Ye, Tracey A. Martin CCMRC, Cardiff University School of Medicine, Cardiff, Wales, UK Introduction. Dual specific phosphatases (DUSPs) are a large family of enzyme able to trigger tyrosine phosphorylation, and phosphorylation of serine and/or threonine. This feature of a dual phosphorylation pattern by DUSPs, renders them with diverse roles in cells, from cytoskeletal regulation of cell migration, regulation of multiple kinases in cell signalling, to regulation of proteins such as microtubules and cell growth. We have investigated DUSP family members, in particular DUSP-7 (otherwise known as PYST2), identified from our studies as being one of prominent DUSPs found to be highly aberrant in certain cancer types including gastric cancer and pancreatic cancer. In breast cancer cells, DUSP7 has been reported as a potential factor linked to drug sensitivity. The role played by DUSP7 in vascular endothelial cells has been poorly studied. Here, we present our findings on the role of DUSP-7 in the regulation of biological functions of vascular endothelial cells. Methods. Human vascular endothelial cells were used in the present study. Cell sub-models were created by knock down of DUSP7. The cells were evaluated for their biological response to loss of DUSP7, including proliferation, migration, matrix adhesion and response to downstream signalling. DUSP7 responses to various exogenous factors were also evaluated by a protein kinase platform. Results. Knock down of DUSP7 in human endothelial cells rendered them manifesting significantly reduced matrix adhesiveness (p< 0.05) and most strikingly, reduced cellular migration (P< 0.0001, control versus DUSP7 knock down). Interestingly, there was little change with the rate of cell growth. When cells were tested by inhibiting a tight junctional molecule, HAVcR1, the endothelial cells responded with markedly reduced DUSP7 (reduction of 46%). Likewise, the cells were also exhibited reduced levels of DUSP7 after being challenged by exogenous metastasis related Kisspeptin, indicating that DUSP7 may participated the wide network of signalling and functional regulations shared with the tight junction regulator HAVcR1 and possibly utilising Mitogen-activated protein kinase 3 (MAPK3 or Extracellular signal regulated kinase-1). Conclusion. Dual specific phosphatase-7 has an important role to play in vascular endothelial cells, primarily on cell matrix adhesion and cellular migration. A possible route of regulation is by targeting the extracellular signalling network. Citation Format: WENXIAO JI, Wen G. Jiang, Lin Ye, Tracey A. Martin. Dual Specific Phosphatase-7 (DUSP7/PYST2) and its role in regulating vascular endothelial functions [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-16-09.

IoT Supervised PV-HVDC Combined Wide Area Power Network Security Scheme Using Wavelet-Neuro Analysis

Power system networks are one of the most widely used methods in the real world for transferring large amounts of electrical energy from one location to another. At present, High Voltage Direct Current Transmission is preferred for long distances over hundreds of miles due to minimal power loss and transmission cost of transmission.Due to an increase in power demand, integration of renewable sources to minimise the voltage fluctuations and compensate for power loss is necessary. This is a mandatory requirement to produce sophisticated protection methods for mainly smart systems under various balanced and unbalanced fault conditions. The system protection scheme must respond as quickly as possible to protect the connected devices in a smart environment. The network must be monitored and protected under various weather conditions as well as electrical parametric problems. The proposed research work is carried on the basis of physical monitoring with the aid of the Internet-of-Things and electrical parameters calibrated with the help of wavelet analysis. A wavelet is a mathematical tool to investigate the behaviour of transient signals at different frequencies, which provides important information related to the detailed analysis of faults in power networks. The major goals of this research are to analyse faults using detailed coefficients of current signals through the bior-1.5 mother wavelet for fault identification and artificial neural network analysis for fault localization. This proposed approach furnishes an IoT supervised Photovoltaic - High Voltage Direct Current (HVDC) combined wide area power network security scheme using wavelet detailed coefficients under various types of faults with Fault-Inception-Angles.

Становление и развитие системы здравоохранения в Горном Алтае в 1930-е гг.

The Altai Mountains were one of the national outskirts of Russia to which special attention was paid, as the indigenous population suffered particularly from epidemics due to their low standard of living and lack of immunity to many diseases. The meagre local budgets severely affected the population's material situation, social and medical services and led to an increased rate of disease. The Soviet authorities began to solve urgent problems in the sphere of public health protection. A consistent policy of the state in this sphere was expressed in the provision of sanitary and epidemiological well-being of the country, addressing the problems of the indigenous peoples of the national regions. The establishment and development of the health care system in the Altai Mountains took place in the context of measures taken by the state to solve these problems. With the help of state and local budgets, the organisation of the medical network emerged and developed, and certain socially dangerous diseases were successfully eliminated. The material and technical base of medical institutions was gradually formed. During this period the following important results in the field of health care have been achieved in the Altai Mountains: a wide network of stationary and mobile medical institutions: hospitals, medical and obstetric stations, antenatal clinics, specialized anti-epidemic institutions and sanatoriums; systematic training of medical personnel had started: medical staff was supplemented mainly through medical staff training, another way of training was the sending of fellows to higher and technical schools. These measures are an important step in the development of the health care system in the region. However, the regional characteristics of Oirotia, namely the patriarchal clan structure of much of the indigenous population, the nomadic way of life, the dispersion of the population and the small number of large settlements, have to a certain extent hindered the process of health care among the indigenous population.

Influence of social support and stressful life events on adherence to colorectal cancer screening using faecal occult blood test in Spanish medium risk population

IntroductionColorectal cancer (CRC) is the second cause of cancer-related deaths worldwide. Five-year survival rate in Spain is 57%. The most important prognostic factor is the stage of the tumor at the diagnosis. CRC can be early diagnosed, but the adherence to screening programs is low (<50%). This study aims to ascertain the influence of social support and stressful life events on the adherence to the population screening of CRC with fecal occult blood test in Spanish average risk population. MethodsMulticenter case–control study. We conducted a simple random sampling among individuals invited to participate in the colorectal cancer screening program. We analyzed epidemiological and social variables associated with lifestyle and behavioral factors. We performed a descriptive and a bivariant analyses and a logistic regression analysis. ResultsFour hundred and eight patients (237 cases and 171 controls) were included. Multivariant analyses showed independent association between higher adherence to the screening program and older age (OR: 1.06; 95% CI: 1.01–1.10), stable partner (OR: 1.77, 95% CI: 1.08–2.89) and wide social network (OR: 1.68; 95% CI: 1.07–2.66). Otherwise, lower adherence was associated to perceiving barriers to participate in the program (OR: 0.92; 95% CI: 0.88–0.96). We find a statistically significant association between lower adherence and high impact stressful life events in the bivariant analyses, and the tendency was maintained (OR: 0.63, 95% CI: 0.37–1.08) in the multivariant. ConclusionSocial variables decisively influence the adherence to colorectal cancer screening. The implementation of social interventions that improve social support, reduce impact of stressful life events and the design of screening programs that decrease the perceived barriers, will contribute to increase the participation on these programs. Secondary, the colorectal cancer diagnosis will be made in early-stages with the consequent mortality reduction.

Adhesion and biofilm formation by the opportunistic pathogen Candida tropicalis: what do we know?

Candida tropicalisis among the most importantCandidaspecies in terms of epidemiology, virulence and resistance. Considering the increase in C. tropicalis incidence and high rates of mortality associated with this species, knowledge of its adhesion and biofilm formation abilities is needed. These traits determine the persistence and survival of yeast on different indwelling medical devices and host sites. C. tropicalis is among the most adherent Candida species, and it has been described as a strong biofilm producer. Environmental factors, phenotypic switching and quorum sensing molecules can affect adhesion and biofilm growth. C. tropicalis can form sexual biofilms, which are promoted by mating pheromones. C. tropicalis biofilms are regulated by a wide and complex network of genes and signaling pathways that are currently poorly understood. Morphological studies showed improved biofilm architecture, which was related to the expression of several hypha-specific genes. Based on recent updates, research is still needed to increase our knowledge on the genetic network of adhesion and biofilm formation by C. tropicalis, as well as the protein diversity that mediates interactions with inert materials and biological surfaces. Here, we have reviewed the main aspects related to adhesion and biofilm formation in C. tropicalis and summarized current knowledge on the significance of these virulence factors in this opportunistic species.

Bearing Fault Diagnosis Based on Wide Deep Convolutional Neural Network and Long Short Term Memory

Bearing Fault Diagnosis Based on Wide Deep Convolutional Neural Network and Long Short Term Memory

Early reading skills and the ventral occipito-temporal cortex organization

Learning to read impacts the way the ventral occipitotemporal cortex (VOT) reorganizes. The postulated underlying mechanism of neuronal recycling was recently revisited. Neuroimaging data showed that voxels weakly specialized for visual processing keep their initial category selectivity (i.e., object or face processing) while acquiring an additional and stronger responsivity to written words. Here, we examined a large and diverse group of six-year-olds prior to formal literacy training (N = 72) using various data analysis techniques (univariate, multivariate, rapid adaptation) and types of stimuli (print, false fonts, houses, faces) to further explore how VOT changes and adapts to the novel skill of reading. We found that among several visual stimuli categories only print activated a wide network of language related areas outside of the bilateral visual cortex, and the level of reading skill was related to the strength of this activation, showing the development of the reading circuit. Rapid adaptation was not directly related to the level of reading skill in the young children studied here, but it clearly revealed the emergence of the reading network in readers. Most importantly, we found that the reorganization of the VOT is not in fact an “invasion” by reading acquisition–voxels previously activated for faces started to respond more for print, while at the same time keeping their previous function. We can thus conclude that the revised hypothesis of neuronal recycling is supported by our data.

Methodologic Recommendations to Implement Pavement Management Systems and Eco-Sustainable Solutions for Local Road Administrations

Local Road Administrations (LRA) manage wide and fragmented road networks with constrained financial and human resources. Though LRA manage the most road networks and the relative development and maintenance have a huge impact on environment and society, frequently, LRA cannot implement new technologies and methodological advancements because innovations are generally fitted to different kinds of users. For these reasons, the adoption of a customised pavement management system (PMS) for LRA is strongly recommended to define workflows, make investments, and find long-lasting and cost-effective solutions. Considering the goal of a sustainable development, new complex aspects must be also considered in the PMS matching policies, investment strategies and engineered solutions. Eco-sustainable techniques for the maintenance of road pavements and utility trenches must be gradually introduced in PMS involving stakeholders to preserve natural resources, to reduce atmospheric pollutions and to stimulate the local economic growth. The definition of a PMS guideline for LRA considering new concepts of a circular economy is a contemporary and open challenge. This paper shows a new PMS approach for LRA, including the strategy and requirements for environmentally friendly road materials that can be progressively adopted by each LRA to lessen the negative effects of maintenance activities on the environment.

Third Country Access to EU Agencies: Exploring Spaces for Influence

AbstractEU decentralised agencies dispose of a wide network of external relations giving third countries various degrees of access to their management boards, secondary bodies and/or operational cooperation. These venues constitute hitherto under‐explored opportunities for third country influence on EU laws and policies. Based on an original dataset, this article maps the widening scope and depth of third country de jure access to 26 EU agencies over time and provides first explanations for the variation across agencies and countries. We find that agency autonomy and international mandate as well as third country democracy, regulatory capacity, and wealth are positively correlated with access. Whilst the de jure provisions examined in this article capture the formal institutional scope for third country influence, they underline the potential for more detailed case studies on de facto third country influence and its determinants.