The connectivity of the brain is continuously adjusted to new environmental influences by several activity-dependent adaptive processes. The most investigated adaptive mechanism is activity-dependent functional or synaptic plasticity regulating the transmission efficacy of existing synapses. Another important but less prominently discussed adaptive process is structural plasticity, which changes the connectivity by the formation and deletion of synapses. In this review, we show, based on experimental evidence, that structural plasticity can be classified similar to synaptic plasticity into two categories: (i) Hebbian structural plasticity, which leads to an increase (decrease) of the number of synapses during phases of high (low) neuronal activity and (ii) homeostatic structural plasticity, which balances these changes by removing and adding synapses. Furthermore, based on experimental and theoretical insights, we argue that each type of structural plasticity fulfills a different function. While Hebbian structural changes enhance memory lifetime, storage capacity, and memory robustness, homeostatic structural plasticity self-organizes the connectivity of the neural network to assure stability. However, the link between functional synaptic and structural plasticity as well as the detailed interactions between Hebbian and homeostatic structural plasticity are more complex. This implies even richer dynamics requiring further experimental and theoretical investigations. Information from the environment leads to the activation of neural subnetworks in the brain. The connectivity of these neural subnetworks, i.e., the existence and strength of synapses between neurons, influences the neuronal activation and, thereby, determines the way environmental information is processed. Accordingly, the long-term storage of information is related to activity-dependent (long-lasting) changes in connectivity Hebb, . BasicalThe alterations of the transmission efficacy by synaptic plasticity depend on the level of neuronal activation. However, the mapping between activity level and triggered synaptic changes is not unique. In general, they are categorized into two classes: Hebbian and homeostatic synaptic plasticity. Hebbian synaptic plasticity yields an increase in synaptic efficacy given high neuronal activities  One class of structural changes maps features of the neuronal activity onto the connectivity, such that the connectivity is strengthened with high activity levels and vice versa. These changes will be referred to as Hebbian structural plasticity Hebb, . (ii) ThNote, this classification is phenomenological. Changes in connectivity  are not directly linked to neuronal activity. Neuronal activity initiates such changes by triggering secondary processes as molecular signaling cascades, which lead to the corresponding changes. For the here discussed plasticity processes, these underlying signaling cascades can have different degrees of similarity, which we will not consider in detail. The focus of this review is to systematize the qualitative links between the neuronal activity level and resulting connectivity changes.Moreover, we focus on morphological changes of connections between excitatory neurons only. The dynamics of inhibitory synapses has been reviewed, for instance, by Vogels et al.  for inhiIn the following, as structural and synaptic plasticity are linked to each other, we first briefly outline the main findings for synaptic plasticity. Then, we review the morphological changes of synapses induced by synaptic plasticity and relate these changes to the dynamics of synapses and, thus, to structural plasticity. Following this, we summarize the experimental evidence of activity-dependent structural changes and categorize these, similar to synaptic plasticity, into the two classes of Hebbian and homeostatic structural plasticity. We also briefly review indications of Hebbian and homeostatic processes occurring during development. Finally, we sort theoretical investigations studying the dynamics of structural plasticity by this categorization and, based on their results, arrive at conclusions about the different functional roles of Hebbian and homeostatic structural plasticity.The most investigated long-term plasticity in neuronal systems is synaptic plasticity. This mechanism adapts synaptic efficacies  between neurons dependent on the neuronal activation. One distinguishes between two different forms of synaptic plasticity: (i) Hebbian synaptic plasticity and (ii) homeostatic synaptic plasticity.Hebbian synaptic plasticity adapts the synaptic efficacies seconds or minutes after onset of a stimulus-induced neuronal activation. In general, neuronal activity induces a calcium influx into the postsynaptic site inducing a complex molecular cascade which changes, amongst others, the number of AMPA receptors determining the synaptic efficacy , which are, in turn, stabilized by homeostatic processes .neurites  grow and retract dependent on the level of neuronal activation  are continuously formed and deleted. Although an axon and a dendrite lie close together and the gap could be bridged by a synapse, the existence of a synapse is not guaranteed  and homeostatic  structural plasticity in adult networks. In addition, we will show that many of these experiments support the here discussed link between synaptic and structural plasticity. We will also provide a brief survey of structural dynamics during development. Finally, we will discuss experimental evidence of the interaction of Hebbian and homeostatic structural plasticity in the same neural system.The induction of LTP by a strong neuronal activation is mainly associated with the increase of the synaptic efficacy  of existing synapses  spines, the stabilization of new spines by the strong stimulus would lead to an increase of the the number of spines as observed experimentally. For this, the rate of forming new spines could be independent of the neuronal activity and stay constant. This potential explanation of the increase in spine number is supported by a recent study demonstrating that LTP stabilizes nascent spines  yields a separation of pre- and postsynaptic terminals  or the probability distribution of the number of synapses between two neurons) to graph theoretical features  which can be compared to biological data. Many studies also investigate functional consequences of structural plasticity as, for instance, the influence on the storage capacity or the ability to classify different inputs. The majority of studies focuses on either Hebbian or homeostatic structural plasticity, however, at the end of this section, we will provide an overview of the few studies combining both processes of structural plasticity.As discussed above, Hebbian structural plasticity is mainly realized by the dynamics of dendritic spines. Thus, models of Hebbian structural plasticity typically describe the dynamics of dendritic spines. Synapses in these models appear and disappear at predefined potential synaptic locations with certain probabilities influenced by neuronal activities, synaptic efficacies and/or other hidden variables. As activities and efficacies depend on synaptic plasticity, Hebbian structural plasticity and Hebbian synaptic plasticity are strongly interconnected and the majority of models of Hebbian structural plasticity also incorporate the dynamics of Hebbian synaptic plasticity and some even homeostatic synaptic plasticity.multiple synapses (four to five synapses). This finding does not depend on the detailed anatomy of neurons, as the number of potential synapse location is much higher than the number of realized synapses  is non-trivial . Here, the stability of synapses depends on the activity-dependent calcium influx; a high calcium influx causes stabilization of synapses and a low influx implies destabilization of synapses. Similar, as for the multi-synaptic connections, high neuronal activities lead to a stabilization of all synapses  start to connect with active neurons at the border of the zone  calcium levels. Even more strikingly, the synaptic transmission efficacies are related to the stability of the synapses. Thus, structural plasticity and its influences on the dynamics of neural networks can only be understood in conjunction with synaptic plasticity.These complex interactions and their functional implications are best understood by theoretical models. For instance, Hebbian structural plasticity seems to remove and create synapses selectively. This selectivity leads to experimentally measured local connectivities and, furthermore, enhances memory lifetime, storage capacity, and robustness. For this, especially the pruning of non-needed synapses plays an important role. However, as for synaptic plasticity, these Hebbian dynamics lead to a positive feedback between connectivity and activity and, thus, to increasing neuronal activities. Thus, also structural plasticity requires a homeostatic process regulating activities back to an intermediate level. Accordingly, theoretical studies show that homeostatic structural plasticity organizes the connectivity of the network to maintain network stability. The combination of Hebbian and homeostatic structural plasticity preserves and improves network functions, as memory storage and input discrimination, and, in parallel, stabilizes the global dynamics in a resource efficient manner.Still there are many open questions summarized in the following. On the experimental side, it is, for example, still unclear whether the increased number of spines after LTP-inducing stimuli results from increased stabilization or from an increased spine formation. Also, the relation between structural and synaptic plasticity is still not completely understood. Along these lines, especially the relation between homeostatic structural plasticity and synaptic scaling has not been completely unraveled yet.Furthermore, we argued that the interaction of Hebbian and homeostatic structural plasticity will lead to a transient increase (decrease) of, e.g., the number of spines in a system which undergoes prolonged phases of enhanced (decreased) activity. Such transient increases are observed in experiments. However, experimental investigations, whether the dynamics occur due to this interaction, are still missing. In general, the interaction of Hebbian and homeostatic processes in the same system is difficult to tackle and has been addressed only by a few studies.Accordingly, also most of the theoretical studies have focused on either Hebbian or homeostatic structural plasticity. The interaction of both mechanisms, especially in recurrent networks, is widely unknown. Moreover, theoretical studies are often restricted to either reproducing biological data or investigating functional consequences of structural plasticity. Therefore, more studies are needed to link experimentally obtained connectivity features to functional predictions.The here reviewed theoretical models mostly considered point-neurons. However, the actual position or location of a synapse on the dendritic tree strongly influences the details of synaptic plasticity  Germany under grant number 01GQ1005B [CT] and 01GQ1005A [MF], and by the Göttingen Graduate School for Neuroscience and Molecular Biosciences (DFG Grant GSC226/2) [MF].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed. IntroductionThe need and significance of protein-based network biomarkers–Human protein–protein interaction network–Methodologies for integrating and identifying network biomarkers–Network biomarker studies in humans–Better understanding of molecular mechanism of diseasesThe development of protein-based network biomarkersCorrelation between network biomarkers and clinical informaticsThe advantages and constraints for clinical applicationProspective and conclusionsThe disease consists of multiple dysfunctional proteins, cells, organs and systems of the body within the complexity and molecular mechanisms by which diseases occur remain unclear, although biotechnologies and knowledge on diseases have been improved tremendously. Given the functional interdependencies between the molecular components, a disease is rarely a consequence of an abnormality of a gene, protein or cell, but reflects the interactions of genes, proteins or cells in a complex network Biomarkers are typically thought of as individual genes, proteins and metabolites (molecular biomarkers). However, with the recent innovation and progress of new biotechnologies, a new type of biomarkers, protein-based network biomarkers Furthermore, the human biomarker discovery mandates the association of biological measurements with clinical information, ideally both statistically and mechanistically. Unlike biological data, clinical data, such as patient complaints, history, therapies, clinical symptoms and signs, physical examinations, biochemical analyses, imaging profiles, pathologies and other measurements, are descriptive and far less structured. The lack of integrative results in the loss or neglecting of valuable information, necessitates novel strategies to successfully combine these large collections of heterogeneous data sets, and identifies new disease-specific biomarkers. To address this issue, clinical bioinformatics was proposed as a new emerging science to combine clinical phenotypes with human tissue-generated bioinformatics and define relationships between collectively directs global function with clinical measurements The present review aims to introduce the need and significance of protein-based network biomarkers, highlight the development of network biomarkers of human diseases, and discuss the clinical relevance and correlation between DNBs and clinical informatics. We will explore how network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, and the potential values of network biomarkers in the future.Biomarkers play an important role in the diagnosis of diseases, and in assessing prognosis and directing treatment of the diseases. Its value is correlated with the disease-associated specificity, sensitivity, traceability, stability, repeatability and reliability. Conventional molecular biomarkers consist of single or a group of several biological molecules such as genes, RNAs, proteins and metabolites that can be measured to distinguish disease from health. Advances in high-throughput technologies such as genomics and proteomics make it possible to measure thousands of different variables in pathobiological conditions simultaneously, providing comprehensive and substantial information of a disease state. An increasing number of biomarkers identified through analysis of expression profiles have been seen. Unfortunately, this type of biomarker lists that obtained from omics data for similarly diagnosed patients by different research groups differ widely and share few in common i.e. PPI networks, RNA networks, metabolic networks and regulatory networks) to contextualize the differential expression patterns observed in a phenotype. The system-based ‘network biomarkers’ The small overlap of biomarkers for similar phenotypes may have various technique reasons, such as platforms differences, samples differences, protocols differences, and statistical reasons, leading to unstable selection of the biomarkers. Besides, identification of a phenotype-associated pathway solely on the basis of differentially expressed molecules is frequently difficult because of the high interdependency of the omics data. But more importantly, these biomarkers are not identified from the systems perspective. With the rapid growing knowledge of the cellular molecular network in diseases, most of the diseases are not considered to be caused by a single effector gene product but the interrelated malfunction of genes and proteins Studies have also integrated multiple data types to generate more accurate molecular networks of diseases et al. I was constructed to computationally identify DNB from high-throughput omic data:d is the numerical measurement of the average Pearson's correlation coefficient among the molecules of DNB in absolute value; PCCo is the numerical measurement of the average Pearson's correlation coefficient of the molecules of DNB with the other molecules in absolute value; SDd is the numerical measurement of the average standard deviation of the molecules of DNB. The composite index I was shown to be effective to provide reliable and significant early warning signal, despite the stochastically fluctuation in the expression of each molecule, in complex diseases such as acute lung injury, liver cancer and lymphoma Chen Figurei) obtaining global expression profiles of a disease phenotype; (ii) integrating such information into protein network frameworks or literature-curated pathways that contain key pathobiological events of phenotypes and (iii) interpreting, identifying and validating phenotype-associated candidate key network molecules or modules  which can facilitate literature curation standards, data exchange and comparison. However, considering the full human protein interaction network has been estimated to be between 154,000 and 369,000 Human PPI network mapping is a crucial component of systematic approach for protein-based network biomarker discovery. Extensive efforts have been seen recently to increase the coverage of human PPI maps by high-throughput yeast two-hybrid Y2H) interaction mating H interacvia the combination of genomics, proteomics and bioinformatics, such as GRNInfer A disease can be linked to a well-defined neighbourhood of PPI network, which refers to as ‘disease module’, representing a panel of network components responsible for cellular function and disruption of which results in a specific disease phenotype Methodologies for expression data integration could be categorized as ‘univariate’ or ‘multivariate’ on basis of statistics via a context-specific PPI network. This approach is effective in characterizing the functional dependency or crosstalk between pathways provided differentially expressions are detected. Unlike the conventional clustering approaches based on differential expressions, classification of differential interactions Briefly, disease-related molecules identified from omic profiling studies or other sources, which serve as the ‘seeds’ of the disease module, are placed on their corresponding proteins in the properly constructed PPI network, and by exploiting both the functional and topological modularity of the network through quantitative systematic approaches, subnetworks and/or pathways with the disease-related components could then be identified as disease module, and as potential network biomarker. Numerous computational methods and algorithms have been proposed for network biomarker identification. For instance, the DDN analysis et al. et al. Variants of aforementioned approaches have been applied to a wide range of disease phenotypes for identifying protein-based network biomarkers, such as breast cancer et al. Integrating multiple types of data sources could enhance the accuracy of the network and improve the quality of identified disease-specific biomarkers et al. et al. The network biomarkers have also been investigated dynamically. Taylor TableThe integration of disease-specific molecules into the knowledge-based protein networks and subnetworks is a new and better way to understand mechanistic hypotheses about the causes of disease. The interactions within such subnetworks are often suggestive of functional signalling cascades, metabolic pathways or molecular complexes responsible for or/and contributing to the phenotypes and dysfunction of the disease. Thus, the network approach offers a potent means of mapping the molecular mechanisms underlying complex pathobiological processes. While the networks of genes and proteins present the links and association between them, such knowledge-integrated interaction is relatively defined and fixed. Rather than only the expression, the strength of interactions between genes or proteins varies during the development of diseases. Moreover, DNB is proved to be the leading network to initiate the critical transition during disease progression, and is highly related to causal factors of the disease et al. et al. Xue I) described above, researchers found two dynamical network biomarkers which can be separately used to predict the peri-insulitis of the early stage of disease and the onset of type 1 diabetes with overt hyperglycaemia et al. Based on the computational algorithm  as networks, and detected a subgroup of patients featuring remarkable abnormalities in a network of serum primary fatty acid amides. The identified disease-associated patterns of biomarkers were suggested to be able to describe the complicity of psychiatric diseases. This particular study demonstrated that simultaneous evaluation of clinical data and molecular biology data via a clinical bioinformatics approach could improve the understanding of complex diseases and lead to better diagnosis, prediction and therapy.The strategies to integrate biological and clinical data have been proposed and are still under the rapid development et al. Because of the large and independent nature of the clinical data, the application of controlled vocabulary and ontology for the standard nomenclature of clinical trial data is critical and important for clinical data integration Using similar approach, the authors further investigated the potential correlation of proteomic profile with clinical informatics in COPD patients. Plasma samples from 18 patients including healthy individuals or patients with sCOPD or AECOPD were collected to measure 507 inflammatory mediators using antibody microarray Protein-based network biomarkers with systems information can provide a more precise and complete profile of cellular changes in human diseases. As proteins perform the major cellular functions essential to signal transduction that role cell growth, differentiation, proliferation and death, protein-based network biomarkers are critical in providing valuable information at the post-translational level that can be used to establish diagnosis or prognosis of a disease and to develop personalized therapeutics, with favourable clinical feasibilities. The effectiveness of protein-based network biomarkers has been demonstrated in the context of various diseases.On the other hand, a number of proteomics-associated challenges should be bear in mind. For instance, proteomics experiments typically screen only a limited fraction of proteins, in particular, gel-based expression experiments are most likely to detect high abundance proteins. The human PPI data are still incomplete and variable because of different curated collections. Therefore, the advantages of the network biomarker discovery include not only easily avoiding data noises by knowledge-based network but also deriving high confident network biomarkers.A number of challenges still exist in every step of the network biomarker development pipelines, despite of technological advances. Protein post-translational modification and alterations in protein stability may influence network modularity on a global scale during disease progression. Most high-throughput methods can suffer from high false-positive or -negative rates that may lead to misclassifications. Large-scale networks are not specific to diseases or clinical phenotypes. The level of certainty is constrained by the issues of data collection, interpretation of large size of the proteome, and the diversity of cells and tissues. On the other hand, noise generated during different network methodologies integration remains a major constraint to correctly interpret complex networks and needs to be critically evaluated and managed. Thus, the principles used in network discovery, validation and development remains to be further defined and quantified, and the development of novel and reliable statistical tools for the network environment is urgently needed. Also, the lack of standardized vocabularies for a definitive translation of networks into the clinical arena represents a main challenge in the integration and interpretation of clinical bioinformatics. Another important challenge is to translate DNBs into the understanding of clinical phenotypes, molecular mechanisms of disease development and progress, and development of therapeutic strategy Despite these limitations related to knowledge incompleteness and uncertainty in the network inference process, the characterization of complex biological phenomena on the basis of functional modular architectures and topological parameters present us with new opportunities to improve our understanding of the aetiology, evolution and therapeutics of the diseases. To reach clinical application, the advantages and disadvantages of protein-based network biomarkers should be furthermore investigated to evaluate the potential values of network biomarkers in the development. We believe that clinical bioinformatics can play an important role in identification and validation of disease-specific DNBs.in vitro/in silico modelling, drug discovery and target screening, and ultimately improving the outcome and life quality of patients.Better biomarkers are urgently needed to disease detection, diagnosis and prognosis. Network approaches have revolutionized the traditional ways of biomarkers discovery and offered a powerful way for pathway mapping and development of disease-specific biomarkers. Although challenges exist in steps of the network biomarker development, network biomarkers are proving to play importance roles in disease-causing genes prediction, disease-related subnetworks identification, disease classification, disease In the era of network medicine, new biomarker discovery depends on a comprehensive view of transcriptome, genome, proteome, and metabolome, or diseaseome. The dynamic nature of human protein interaction network because of the diversity and regulative structure of post-translational modifications, gives in-depth insight into disease mechanism. Scientists and physicians are facing more challenges to keep the pace with the growing availability of a variety of high-throughput data. Global efforts are been done to improve our understanding of diseases through integrative approaches to translate science into the clinical practice


Chronic stress-related psychiatric diseases, such as major depression, posttraumatic stress disorder, and schizophrenia, are characterized by a maladaptive organization of behavioral responses that strongly affect the well-being of patients. Current evidence suggests that a functional impairment of the prefrontal cortex (PFC) is implicated in the pathophysiology of these diseases. Therefore, chronic stress may impair PFC functions required for the adaptive orchestration of behavioral responses. In the present review, we integrate evidence obtained from cognitive neuroscience with neurophysiological research with animal models, to put forward a hypothesis that addresses stress-induced behavioral dysfunctions observed in stress-related neuropsychiatric disorders. We propose that chronic stress impairs mechanisms involved in neuronal functional connectivity in the PFC that are required for the formation of adaptive representations for the execution of adaptive behavioral responses. These considerations could be particularly relevant for understanding the pathophysiology of chronic stress-related neuropsychiatric disorders. The enriched image we have of the world as well as the great number of behavioral options implies the need for appropriate and adaptive accommodation of behavioral responses for our survival. This demand is especially evident in neuropsychiatric disorders like major depressive disorder (MDD), anxiety disorders , and schizophrenia (SZ), in which subjects develop aberrant and maladaptive behavioral responses which negatively affects well-being . ForThe adaptive organization of goal-directed behavioral responses is supported by the prefrontal cortex (PFC) , 10 and In the present review we integrate theoretical considerations and empirical evidence, mainly from rodents animal models, to relate the effects of chronic stress on neural functional connectivity related to the organization of behavior mediated by the PFC. In The first clues about the role of the PFC came from studies of PFC lesions in humans. Lesions of the PFC have long been known to produce impairment of cognitive functions, such as attention, set-shifting, working memory, planning, temporal integration, decision making, retrieving and manipulating old memories, and inhibitory control (reviewed extensively in ). AlthouThe lesion studies of the PFC in both primates and rodents led to the idea that the principle and more general behavioral impairments associated with PFC lesions are not explained by failure of a particular function but imply a failure to coordinate a set of cognitive processes. A large body of evidence leads to the hypothesis that the main function of the PFC is cognitive control of behavior, that is, the adaptive accommodation of behavioral responses to current perceptual conditions toward the attainment of goals , also knTo the execution of the perception-action cycle, both the primate PFC and rodent mPFC are anatomically positioned between the perceptual input and motor and visceral output neural systems . The perPerceptual information about the external and internal environment is transmitted mainly from the orbitofrontal cortex (OFC) and the insula, respectively . The PFCFor the execution of the perception-action cycle, the PFC projects to neural systems involved in generating behavioral responses and executing actions , 53. TheIt is important to take into consideration that, based on anatomical and physiological criteria, both the primate and rodent PFC may be subdivided into different compartments which could have different behavioral roles Theoretical work by Fuster , 57, 58 In a first step, through selective attention, the perceptual information relevant to the goal is focused and selected. During the acquisition of the perception-action cycle, the PFC recalls and engages sparse representations of perception and of actions stored in different brain systems . It also assumes that the neural assembly is formed by the activity-dependent repeated coactivation of a group of neurons during behavior.Activation of the neural assembly correlates to behavior. This assumes that behavioral performance is paralleled with activation of the neural assembly, thus providing a functional meaning to the assembly.However, due to variability of firing rates and limited representation capacity , the sinTherefore, a neural assembly that represents a cognitive map in the PFC is composed of functional connectivity among neurons whose coordinated activity reflects learned relationships with cognitive-relevant elements, such as strategies, decisions, and goals . ExperimThe emergence of neuronal assemblies related to executive functions like working memory has been well demonstrated in the rodent mPFC –98. The The rodent mPFC is subdivided into PL and IL, and it has been suggested that both subdivisions have particular behavioral roles , 52, 53.It has been suggested that dysfunction in neural assemblies is associated with stress-related disorders like SZ . Indeed,Hebb's hypothesis proposes that the formation, storage, and reactivation of neural assemblies depend on the alteration of the efficacy and connectivity of synapses in relevant neural networks , a conce in vitro [ in vivo between the mPFC and other brain structures, as the HPC and the thalamus [Short- and long-term synaptic plasticity have been observed in rodent mPFC slicesin vitro , 116–123thalamus –128. Howthalamus  found ththalamus  found th in vivo [ in vivo in the HPC-mPFC axis [It has been proposed that alteration in synaptic plasticity is an important mechanism in the development of stress-related psychiatric disorders –131. Beh in vivo , 136. Ch in vivo , 137 andPFC axis , 139, thPFC axis , and thePFC axis . These dPFC axis , 140. InPFC axis , 143.Whether chronic stress has direct repercussions on synaptic plasticity related to the formation of neural assemblies in the mPFC is still unknown. However, as Kim et al.  showed, Neural oscillations are patterns of rhythmic electrical activity expressed as voltage changes in the extracellular space that arises from complex interactions between neurons and the networks to which they belong . Given tOscillatory activity is significant because neuronal events related to cognitive activity are integrated by oscillatory rhythms . For exaAs described in Oscillatory synchrony between the mPFC and HPC has been observed in rodents , 160–166Alterations in oscillatory activity have been suggested as a critical component in stress-related mental disorders –169. GenAlthough not considered an executive function, the recall of remote memories has been shown to be linked to the PFC , 174. ItCurrently, the most accepted model for memory consolidation is the two-stage model , which pThe proposed biological advantage of this two-stage model is that memories are not consolidated until the significance of an experience has been evaluated . This imNeural assemblies that represent rules or strategies in goal-directed tasks are highly stable in the mPFC even for some time after acquisition, suggesting long-term stability of the neural assembly , 190. ItIt has been suggested that replay is accomplished by oscillatory synchrony between the neocortex and HPC . During Synchrony between the mPFC and HPC also allows the replay of prefrontal neural assemblies during sleep –98. NeurThe amygdala promotes the transmission of information between the HPC and the neocortex needed for consolidation of memories by tagging events that are to be consolidated . InteresSome stress-related psychiatric disorders, like MDD and PTSD, are characterized by an aberrant persistence of aversive memories , 6. ReceIt is important to take into consideration that the aberrant persistence of aversive memories observed in stress related disorders is related to aversive memories , 6 but nChronic stress impairs glutamatergic synaptic transmission , reducinChronic stress impairs also oscillatory coherence between the mPFC and other brain systems  in behaviorally relevant frequency bands  . This imAltogether, these alteration induced by chronic stress may result in the impairment of the formation and consolidation of neural assemblies that represent adaptive cognitive maps in the mPFC, in a similar manner as what occurs with hippocampal place cells ; Section.In both humans and rodent models, these neurophysiological alterations induced by chronic stress result in the impairment of the generation of behavioral responses dependent on the prefrontal network, such as working memory, selective attention, behavioral flexibility, recall of the extinction of conditioned fear, and decision making . These bIn the previous sections we integrate evidence from basic and cognitive neuroscience that approach how the mPFC participates in the implementation of the adaptive organization of behavioral responses. We also put evidence with focus on how chronic stress could affect the organization of behavioral responses mediated by the mPFC. This evidence, systematized in The goal of the present review is to propose a framework that unifies cognitive neuroscience and neurophysiological evidence that attempts to explain the impairment of behavioral responses observed in chronic stress-related mental disorders. As a first proposal, our hypothesis is that the behavioral alterations observed in chronic stress-related psychiatric disorders can be categorized into three classes : (i) exaThe second proposal states that the above-mentioned cognitive defects observed in psychiatric diseases are the result of the anomalous and maladaptive formation of cognitive maps that represent perception-action cycles in the PFC. And the third proposal is that these anomalous representations are the consequence of the chronic stress-induced impairment of the neural mechanisms involved in the formation and/or consolidation of neural assemblies that represent these cognitive maps in the PFC.These impairments result in the development of aberrant cognitive maps that link perceptual information to a repertory of erroneous actions, resulting in the maladaptive behavioral response directed to goals. How are these aberrant cognitive maps formed? In a first step, during the integration of perceptual information by the mPFC, the hyperactivation of the amygdala induced by chronic stress  can exceIn a second step, the long-term storage of these maladaptive cognitive maps may also be favored by chronic stress. Again, hyperactivity of the amygdala may favor synchrony at the theta oscillatory band between the PFC and the HPC during sleep, required to consolidate already formed cognitive maps in the PFC by favoring the replay of existing neural assemblies . The chrIn a third step, chronic stress may enhance activation  of these maladaptive cognitive maps. Upon the presentation of ambiguous perceptual information, chronically stressed subjects activate more consolidated cognitive maps that correspond to previously acquired and consolidated maladaptive maps. This may be the consequence of the activation of neural assemblies that represent previously linked distributed associative events related to the negative-biasing interpretation of environmental information observed in chronic stress-induced disorders.Chronic stress impairs cognitive flexibility as observed in reversal learning and set-shifting tasks , 68, sug per se. Further research will complement and support the current hypothesis. It is necessary to record neuronal assemblies in the mPFC while executing cognitive functions in animals subjected to chronic stress. Large-scale neuronal recording methods [In this review, we integrate evidence from cognitive neuroscience and behavioral neurophysiology with current knowledge of chronic stress-induced impairment in behavioral and neuronal function in the PFC to formulate a hypothesis on cognitive dysfunctions observed in stress-related neuropsychiatric disorders. We hypothesize that chronic stress-induced impairment of neural processes required for structuring neuronal assemblies in the PFC plays an important role in the abnormal organization of behavior observed in chronic stress-induced neuropsychiatric disorders. However, our hypothesis, to date, is mainly sustained by indirect evidence like the effect of chronic stress on the mechanisms involved in configuring neuronal assemblies, rather than observation of the effect of chronic stress on neural assemblies methods  and high methods , togethe methods , 216, wi


Life-cycle (LC) approaches play a significant role in energy policy making to determine the environmental impacts associated with the choice of energy source. Data envelopment analysis (DEA) can be combined with LC approaches to provide quantitative benchmarks that orientate the performance of energy systems towards environmental sustainability, with different implications depending on the selected LC + DEA method. The present paper examines currently available LC + DEA methods and develops a novel method combining carbon footprinting (CFP) and DEA. Thus, the CFP + DEA method is proposed, a five-step structure including data collection for multiple homogenous entities, calculation of target operating points, evaluation of current and target carbon footprints, and result interpretation. As the current context for energy policy implies an anthropocentric perspective with focus on the global warming impact of energy systems, the CFP + DEA method is foreseen to be the most consistent LC + DEA approach to provide benchmarks for energy policy making. The fact that this method relies on the definition of operating points with optimised resource intensity helps to moderate the concerns about the omission of other environmental impacts. Moreover, the CFP + DEA method benefits from CFP specifications in terms of flexibility, understanding, and reporting. The energy sector currently faces severe concerns about energy security and environmental sustainability , 2. HoweIncreasing social awareness on these issues has fostered the research and development of clean and renewable energy systems, as well as the establishment of management schemes to evaluate and improve their environmental performance. According to this socioeconomic context, current energy policies are focused on the promotion of environmentally sustainable energy systems. For instance, at a European level the Renewable Energy Directive 2009/28/EC  and rela2 atmospheric concentration stabilisation target by 2050 are highly bound to the enhancement of energy efficiency and the minimization of fossil fuel use in this sector [Current energy policies tend to identify environmentally sustainable energy with low-carbon energy, limiting the environmental dimension of the energy system to its global warming impact. This fact responds to the recognized need to act on GHG emissions to mitigate climate change, being, therefore, the major socioenvironmental concern to be addressed by energy actors through the whole supply chain , 9. In fs sector , 11.Working at different scales and with interdisciplinary approaches is needed to enhance cleaner production alternatives and technological development, enabling the energy sector to meet the established objectives , 12. ForThe environmental sustainability of an energy system is usually measured in terms of the associated savings in GHG emissions. These GHG savings are calculated based on the global warming impact potential (GWP) of the assessed energy system in comparison with that of the conventional fossil equivalent, following a life cycle (LC) perspective . Due to The completeness of CFP in terms of methodological soundness makes it comparable to the well-established life cycle assessment (LCA) methodology , 19. In LC-based methodologies are of paramount importance in energy policy making, as they arise as a relevant source of criteria to be taken into account when it comes to defining policies and regulations. In particular, these policies often have to state quantitative benchmarks that orientate the performance of specific energy systems towards environmental sustainability. Robust LC-based methodologies that facilitate the quantification of these benchmarks are therefore required. However, while LC-based methods are highly used to understand the relevant sources of environmental impacts within the LC of the assessed system, their utility as single methodologies for ecoefficiency verification and environmental impact minimisation across complex sectoral systems is frail.In this context, data envelopment analysis (DEA) arises as a linear programming methodology to measure the relative efficiency of multiple homogenous entities when the productive process shows a structure composed of multiple inputs and outputs . DEA alsDEA is being increasingly combined with LC approaches to provide LC-based benchmarks. The resultant combined methodologies are of general use and they are only conditioned by the availability of input and output data for a set of multiple homogenous entities, normally called decision-making units (DMUs) . Within Beyond the combined use of LCA and DEA, other LC-based methodologies have been coupled with DEA, leading to the breakthrough of the LC + DEA concept. For example, emergy analysis can be applied in combination with DEA to provide an ecocentric benchmarking tool . HoweverCurrent LC + DEA methods for the benchmarking of multiple DMUs can be divided into two main blocks: those that are inspired on the direct monitoring of environmental benchmarks and those that assess these benchmarks through the computation of energy methods. In other words, the former focus on the benchmarking of environmental indicators, while the latter provide benchmarks expressed in energy terms. To date, two specific methods have been developed regarding environmental LC + DEA methods. On the one hand, the five-step LCA + DEA method leads to the calculation of environmental benchmarks directly associated with the optimised operational performance of the DMUs . This reOn the other hand, the three-step LCA + DEA method addresses the direct benchmarking of the environmental impacts of the DMUs under assessment, also allowing the simultaneous benchmarking of operational items . The datRegarding the available energy LC + DEA methods, three different perspectives have been developed, each of which with distinct features due to the different indicators subject to benchmarking. These methods can be classified according to their anthropocentric or ecocentric perspective.On the one hand, anthropocentric alternatives include the three-step CED + DEA and CExD + DEA methods, which provide benchmarks of cumulative energy demand (CED) and cumulative exergy demand (CExD) indicators, respectively . On the A survey of the main LC + DEA case studies conducted to date is presented in The five-step LCA + DEA method is found to be the methodological approach most often selected by LC + DEA practitioners. To date, this approach has been mainly applied to the primary sector, for example, for agriculture –35, aquaGiven the novelty of the LC + DEA concept, it is expected that new LC + DEA methods may emerge in the future as a result of further research in the fields of environmental management and ecoefficiency. Furthermore, the expected methodological development of social life cycle assessment (SLCA) and life cycle sustainability assessment (LCSA) may provide interesting insights into the use of LC + DEA methods as related to the social and economic pillars of sustainability , 43, 44.A comprehensive evaluation of environmental impact categories (in LCA studies) and/or energy indicators , while providing a solid basis for the determination of a thorough set of benchmarks useful for (energy) policy making, can hinder a single interpretation of the results/benchmarks. When there is a special interest in specific environmental or energy indicators, more straightforward LC + DEA approaches could be conceived. In particular, since energy policies focus on GWP and diverse schemes for the calculation of LC-GHG emissions are available or in progress, a combined method based on CFP and DEA is proposed in this paper: the CFP + DEA method .A five-step method combining CFP and DEA is herein proposed to provide benchmarks for energy policy making by (i) collecting data on the material and energy flows of multiple DMUs, (ii) evaluating the carbon footprint of each current DMU, (iii) benchmarking the operational performance of the DMUs through DEA, (iv) evaluating the carbon footprints of virtual DMUs that incorporate the operational benchmarks, and (v) interpreting the results to provide environmental benchmarks for policy making and additional outcome . A threeAs can be observed by comparing Figures The final step of the CFP + DEA method which deals with the interpretation of the results from the previous steps merits further attention. In particular, the target carbon footprints from step 4 are fed to the fifth step to define the environmental benchmarks needed for energy policy making. Individual benchmarks may be necessary to make policies aimed at a specific set of entities. In other cases, overall benchmarks (based on efficient DMUs) are needed to set general objectives for entities at a larger scale. In those cases in which a large number of evaluated DMUs are found to operate efficiently but the use of a reduced set of CFP benchmarks drawn from the best-performing units is intended for policy making, the complementary use of superefficiency DEA models to discriminate among efficient entities is encouraged , 50.Additional outcome from the final step of the CFP + DEA method includes ecoefficiency verification and the possibility to discuss environmental, economic, and social aspects under the same framework for sustainability assessment . AccordiAll the LC + DEA methods mentioned in this paper allow benchmarking and have the potential of being used for policy making. However, each method involves different benchmarking perspectives and/or focuses on different indicators as benchmarking criteria. A decision flowchart to facilitate the appropriate selection of a specific LC + DEA method for policy making is proposed in As the current context for energy policy making implies an anthropocentric perspective with focus on the environmental concerns, and specifically on GWP, the five-step CFP + DEA method is expected to be the most consistent LC + DEA approach for the provision of benchmarks with energy policy making purposes. planetary boundaries [The reduction in anthropogenic GHG emissions has become a key issue in international environmental policy due to its relevance when it comes to mitigating climate change . Howeverundaries , contribundaries . power down policies [ Zero Carbon Britain report [While reductions in GHG emissions may be attained through multiple schemes, a key strategy in developed countries is the implementation ofpolicies , 58, aimn report , 60. TheLCA-related assessment tools, such as CFP, have shown to be fairly flexible in terms of their methodological implementation for two main reasons. On the one hand, these methods are designed to be used worldwide to address the environmental profile of any product or service. Therefore, these methods need sufficient flexibility to adapt to the specific research questions that arise in site- and product-specific situations.On the other hand, as highlighted by Finkbeiner , there aThe proliferation of CFP standards fostered by different standardisation organisations , the CFP + DEA method constitutes a robust and consistent scheme for the provision of benchmarks for energy policy making. Moreover, the fact that this method relies on the definition of operating points with optimised resource intensity helps to moderate the concerns about the omission of other environmental impacts. Additionally, the CFP + DEA method benefits from CFP specifications in terms of flexibility, understanding, and reporting. Finally, although the CFP + DEA method is presented as a valuable tool to provide benchmarks for analysts, decision makers at company level, and policy makers in the energy sector, this utility can also be extended beyond the energy sector to other sectors with scattered sources of production, such as mining, transport, or fishing.


An external skeleton is an essential part of the body plan of many animals and is thought to be one of the key factors that enabled the great expansion in animal diversity and disparity during the Cambrian explosion. Molluscs are considered ideal to study the evolution of biomineralization because of their diversity of highly complex, robust and patterned shells. The molluscan shell forms externally at the interface of animal and environment, and involves controlled deposition of calcium carbonate within a framework of macromolecules that are secreted from the dorsal mantle epithelium. Despite its deep conservation within Mollusca, the mantle is capable of producing an incredible diversity of shell patterns, and macro- and micro-architectures. Here we review recent developments within the field of molluscan biomineralization, focusing on the genes expressed in the mantle that encode secreted proteins. The so-called mantle secretome appears to regulate shell deposition and patterning and in some cases becomes part of the shell matrix. Recent transcriptomic and proteomic studies have revealed marked differences in the mantle secretomes of even closely-related molluscs; these typically exceed expected differences based on characteristics of the external shell. All mantle secretomes surveyed to date include novel genes encoding lineage-restricted proteins and unique combinations of co-opted ancient genes. A surprisingly large proportion of both ancient and novel secreted proteins containing simple repetitive motifs or domains that are often modular in construction. These repetitive low complexity domains (RLCDs) appear to further promote the evolvability of the mantle secretome, resulting in domain shuffling, expansion and loss. RLCD families further evolve via slippage and other mechanisms associated with repetitive sequences. As analogous types of secreted proteins are expressed in biomineralizing tissues in other animals, insights into the evolution of the genes underlying molluscan shell formation may be applied more broadly to understanding the evolution of metazoan biomineralization. According to the fossil record many animal phyla diversified during the Late Precambrian to Early Cambrian, roughly 515–541 million years ago . VariouMollusca  is one of the most morphologically and ecologically diverse metazoan phyla, with an estimated 200,000 extant species and an evolutionary history tracing back to at least to the Early Cambrian . The greThe adult molluscan shell is a remarkably stable organo-mineral biocomposite, in which the calcium carbonate mineral makes up 95–99 % . In mostengrailed is likely a key member of this GRN, as its expression has been observed at the boundary of non-shell-secreting and shell-secreting cells in the shell field margin of different molluscan classes ) in silico predictions of secreted proteins have been used to identify and compare putative shell matrix proteins (SMPs) in several taxa ),As described above for molluscs, biomineralizing tissues in other animals express a high proportion of rapidly evolving gene families , 137. FoFinally, repetitive sequences similar to those found in molluscs are also common in skeletal matrix proteins in disparate metazoans. RLCD-containing proteins such as collagens, silks, and silk-like proteins are commonly observed in metazoan skeletal matrices, including in the echinoderms , 144, veThe integration of the fields of genomics and proteomics into the study of molluscan biomineralization has revealed that shell formation is controlled by the highly coordinated expression of hundreds of genes, and the regulated secretion of proteins and other macromolecules. Although the dissection of the mantle gene regulatory network controlling shell fabrication is in its infancy, there is evidence, at least in early developmental stages, for a deep conservation of expression patterns of regulatory genes. Despite this apparent deep homology, the diverse array of molluscan shell architectures and patterns indicate that there exist underlying molecular differences that manifest later in the morphogenetic program. One source of this variation is the rapidly-evolving mantle secretome that shows high levels of uniqueness, even in closely related taxa. We propose that as terminal nodes in the mantle GRN, genes encoding the mantle secretome are less constrained and more evolvable, allowing for the intra- and inter-specific variation that underpins the spectacular diversity of molluscan shells.Common principles that govern the molecular basis of skeleton formation are emerging from the analysis of molluscan SMP-encoding genes. These appear to apply broadly across the animal kingdom, and include (i) continuous influx and efflux of conserved secreted gene products, (ii) the evolution and expansion of lineage-specific secreted protein families, and (iii) the presence of highly-evolvable repetitive low complexity domains in both evolutionarily young and old secreted gene products. As in molluscs, these gene classes are likely to sit at the termini of late biomineralization GRNs in other animals. Further insight into how these ancient and novel gene families contribute to the building and patterning of the diversity of molluscan shells is likely to provide guiding principles into the evolution and formation of metazoan skeletons.CA, carbonic anhydrase; GRN, gene regulatory network; KRMP, lysine (K)-rich mantle protein; RLCD, repetitive low-complexity domain; SMP, shell matrix protein


A 3-state syndromic surveillance system documented reduction and emergence of neurologic infectious diseases. Encephalitis is a devastating illness that commonly causes neurologic disability and has a case fatality rate >5% in the United States. An etiologic agent is identified in <50% of cases, making diagnosis challenging. The Centers for Disease Control and Prevention Emerging Infections Program (EIP) Encephalitis Project established syndromic surveillance for encephalitis in New York, California, and Tennessee, with the primary goal of increased identification of causative agents and secondary goals of improvements in treatment and outcome. The project represents the largest cohort of patients with encephalitis studied to date and has influenced case definition and diagnostic evaluation of this condition. Results of this project have provided insight into well-established causal pathogens and identified newer causes of infectious and autoimmune encephalitis. The recognition of a possible relationship between enterovirus D68 and acute flaccid paralysis with myelitis underscores the need for ongoing vigilance for emerging causes of neurologic disease. Encephalitis is a potentially devastating illness: the related case-fatality rate in the United States is >5% , using a case definition adapted from previous studies . The casA major goal of the EIP Encephalitis Project was the implementation of a standardized diagnostic algorithm to be used at all 3 sites. However, early in the course of the project, it was recognized that there were substantial regional differences in the frequency of specific pathogens, such as arboviral and rickettsial infections. The concept of a standardized testing algorithm thus evolved to include a site-specific core set of routinely performed laboratory tests to capture the most common and most treatable etiologies, supplemented by targeted testing on the basis of season, exposures, clinical features, and geography  (9). ForA final unique feature of the EIP Encephalitis Project was the development of defined a priori pathogen-specific criteria to establish causality. Cases were classified as having a possible, probable, or confirmed etiology constructed on the basis of whether the identified pathogen was a well-established cause of encephalitis and whether there was direct evidence of central nervous system infection . AlN-methyl-D-asparate receptor (NMDAR) encephalitis, as the leading cause of encephalitis among patients <30 years of age  with encephalitis studied to date: >4,000 case-patients were enrolled in the California Encephalitis Project and >700 in the Tennessee Unexplained Encephalitis Project. (Cases at the New York site were enrolled for diagnostic testing only.) Despite the rigorous diagnostic testing algorithm, in approximately half of all cases, no underlying cause for encephalitis was identified. Several factors likely contribute to the frustratingly high proportion of cases that had unidentified pathogens. Foremost is that, for many pathogens other than HSV, PCR of cerebrospinal fluid (CSF) was not an optimal diagnostic test. The high sensitivity of PCR in some instances lead to detection of reactivated viruses in CSF of questionable significance, such as Epstein-Barr virus , and varicella zoster virus combined (Before the start of the EIP Encephalitis Project, it was well recognized that paraneoplastic syndromes could cause limbic encephalitis, albeit infrequently. In 2007, Josep Dalmau and colleagues described anti-NMDAR encephalitis, a novel form of autoimmune encephalitis (The widespread implementation of the varicella vaccine in the 1990s has essentially eliminated varicella zoster virus as a cause of pediatric encephalitis (When the EIP Encephalitis Project was initiated, it was unforeseeable that a virus never before identified in the Western Hemisphere would cause an encephalitis epidemic in the United States. Yet, during 1999–2013, more than 17,000 cases of West Nile neuroinvasive disease were diagnosed; the case-fatality rate was 9% (The infrastructure that proved invaluable in enabling a rapid response to the WNV epidemic also was instrumental in identifying an emerging neurologic syndrome characterized by acute flaccid paralysis. In 2012, several physicians familiar with the California Encephalitis Project contacted the project, reporting cases of previously healthy patients with acute onset of a polio-like illness. Routine testing for organisms associated with acute flaccid paralysis returned negative results, raising concern for a novel agent or pathogen causing this syndrome. These sporadic cases occurred at geographically disparate sites and likely would not have been recognized without an existing surveillance mechanism. Ultimately, more than 23 cases were identified in California (The EIP Encephalitis Project has demonstrated the value of syndromic surveillance in a constantly changing environment. Globally, this project represents the largest known cohort of patients with encephalitis. The robust sample size provided sufficient power to investigate recognized pathogens and to identify newer causes of encephalitis, both infectious and autoimmune. Syndromic surveillance confirmed that previously common causes of pediatric encephalitis such as VZV have been all but eliminated by vaccination, and conversely, that childhood immunization is not substantially associated with development of encephalitis. The recognition of an emerging syndrome of acute flaccid paralysis with myelitis, possibly caused by enterovirus D68, underscores the need for ongoing vigilance for emerging causes of neurologic disease.


The functionality of cochlear implants (CI) depends, among others, on the number and excitability of surviving spiral ganglion neurons (SGN). The spatial separation between the SGN, located in the bony axis of the inner ear, and the CI, which is inserted in the scala tympani, results in suboptimal performance of CI patients and may be decreased by attracting the SGN neurites towards the electrode contacts. Neurotrophic factors (NTFs) can support neuronal survival and neurite outgrowth.Since brain-derived neurotrophic factor (BDNF) is well known for its neuroprotective effect and ciliary neurotrophic factor (CNTF) increases neurite outgrowth, we evaluated if the combination of BDNF and CNTF leads to an enhanced neuronal survival with extended neurite outgrowth. Both NTFs were added in effective high concentrations , alone and in combination, to cultured dissociated SGN of neonatal rats for 48 hours.The neuronal survival and neurite outgrowth were significantly higher in SGN treated with the combination of the two NTFs compared to treatment with each factor alone. Additionally, with respect to the morphology, the combination of BDNF and CNTF leads to a significantly higher number of bipolar neurons and a decreased number of neurons without neurites in culture.in vitro and to regenerate retracted nerve fibers.The combination of BDNF and CNTF shows a great potential to increase the neuronal survival and the number of bipolar neurons  Cochlear implants (CI) are the standard therapy for patients suffering from sensory neural hearing loss. In the last years, technical innovations like the development of novel speech processing strategies improved the benefits that patients can obtain with a CI, for example open speech recognition . NeverthNeuronal survival can be increased by electrical stimulation –7 or neuin vitro , butresholds ,59, incr