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In locally advanced (LA) breast cancer (BC), neoadjuvant treatments have led to major achievements, which hold particular relevance in HER2-positive and triple-negative BC. Conversely, their role in hormone receptor positive (HR+), hormone epidermal growth factor 2 negative (HER2-) BC is still under debate, mainly due to the generally low rates of pathological complete response (pCR) and lower accuracy of pCR as predictors of long-term outcomes in this patient subset. While administration of neoadjuvant chemotherapy (NCT) in LA, HR+, HER2- BC patients is widely used in clinical practice, neoadjuvant endocrine therapy (NET) still retains an unfulfilled potential in the management of these subgroups, particularly in elderly and unfit patients. In addition, NET has gained a central role as a platform to test new drugs and predictive biomarkers in previously untreated patients. We herein present historical data regarding Tamoxifen and/or Aromatase Inhibitors and a debate on recent evidence regarding agents such as CDK4/6 and PI3K/mTOR inhibitors in the neoadjuvant setting. We also discuss key issues concerning the optimal treatment length, appropriate comparisons with NCT efficacy and use of NET in premenopausal patients.

The second-order nonlinear optical (NLO) properties of iridium(III) complexes having two cyclometalated 2-phenylpyridines and curcumin or tetrahydrocurcumin as ancillary ligand have been investigated both in solution and as guest in a polymeric organic matrix. In solution, these complexes are characterized by a significant second-order NLO response, as determined by the Electric Field Induced Second Harmonic (EFISH) technique, like the related complex with acetylacetonate. Whereas the low second-harmonic generation response of a composite film of [Ir(2-phenylpyridine)2(acetylacetonate)] in polymethyl methacrylate was not stable and fell down to zero upon turning off the electric field. A good and stable response was obtained with a film based on the iridium(III) complex bearing two cyclometalated 2-phenylpyridines and curcumin.

Whereas there is an increasing amount of reports on the second-order nonlinear optical (NLO) and luminescence properties of tetradentate [N2O2] Schiff base–zinc complexes, the study of zinc complexes having two bidentate [NO] Schiff-base ligands is relatively unexplored from an NLO point of view. This work puts in evidence that the known chiral bis{2-[(R)-(+)-1-phenylethyliminomethyl]phenolato-N,O}zinc(II) complex is a fascinating multifunctional molecular inorganic–organic hybrid material characterized by interesting second-order NLO and luminescent properties in solution. The emissive properties of the organic 2-(R)-(+)-1-phenylethyliminomethyl]phenol proligand are greatly enhanced upon coordination to the inorganic Zn(II) center.

Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

The dynamics and behavior of ferromagnets have a great relevance even beyond the domain of statistical physics. In this work, we propose a Monte Carlo method, based on random graphs, for modeling their dilution. In particular, we focus on ferromagnets with dimension D≥4, which can be approximated by the Curie-Weiss model. Since the latter has as graphic counterpart, a complete graph, a dilution can be in this case viewed as a pruning process. Hence, in order to exploit this mapping, the proposed strategy uses a modified version of the Erdős-Renyi graph model. In doing so, we are able both to simulate a continuous dilution and to realize diluted ferromagnets in one step. The proposed strategy is studied by means of numerical simulations, aiming to analyze main properties and equilibria of the resulting diluted ferromagnets. To conclude, we also provide a brief description of further applications of our strategy in the field of complex networks.

This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.

Group formation is a quite ubiquitous phenomenon across different animal species, whose individuals cluster together forming communities of diverse size. Previous investigations suggest that, in general, this phenomenon might have similar underlying reasons across the interested species, despite genetic and behavioral differences. For instance improving the individual safety (e.g. from predators), and increasing the probability to get food resources. Remarkably, the group size might strongly vary from species to species, e.g. shoals of fishes and herds of lions, and sometimes even within the same species, e.g. tribes and families in human societies. Here we build on previous theories stating that the dynamics of group formation may have evolutionary roots, and we explore this fascinating hypothesis from a purely theoretical perspective, with a model using the framework of Evolutionary Game Theory. In our model we hypothesize that homogeneity constitutes a fundamental ingredient in these dynamics. Accordingly, we study a population that tries to form homogeneous groups, i.e. composed of similar agents. The formation of a group can be interpreted as a strategy. Notably, agents can form a group (receiving a 'group payoff'), or can act individually (receiving an 'individual payoff'). The phase diagram of the modeled population shows a sharp transition between the 'group phase' and the 'individual phase', characterized by a critical 'individual payoff'. Our results then support the hypothesis that the phenomenon of group formation has evolutionary roots.

Organic room temperature persistent luminescence is a fascinating but still largely unexplored phenomenon. Cyclic-triimidazole and its halogenated (Br, I) derivatives have recently revealed as intriguing phosphors characterized by multifaceted emissive behavior including room temperature ultralong phosphorescence (RTUP) associated with the presence of H-aggregates in their crystal structure. Here, we move towards a multicomponent system by incorporating a fluoropyridinic fragment on the cyclic-triimidazole scaffold. Such chromophore enhances the molecular properties resulting in a high photoluminescence quantum yield (PL QY) in solution but preserves the solid-state RTUP. By means of X-ray diffraction (XRD) analysis, theoretical calculations, steady-state and time-resolved spectroscopy on solutions, polymethylmethacrylate (PMMA) blends and crystals, the nature of the different radiative deactivation channels of the compound has been disclosed. In particular, the molecular fluorescence and phosphorescence, this latter observed in frozen solution and in PMMA blends, are associated to deactivation from S1 and T1 respectively, while the low energy RTUP, observed only for crystals, is interpreted as originated from H aggregates.

Heart rate variability (HRV; variability of the RR interval of the electrocardiogram) results from the activity of several coexisting control mechanisms, which involve the influence of respiration (RESP) and systolic blood pressure (SBP) oscillations operating across multiple temporal scales and changing in different physiological states. In this study, multiscale information decomposition is used to dissect the physiological mechanisms related to the genesis of HRV in 78 young volunteers monitored at rest and during postural and mental stress evoked by head-up tilt (HUT) and mental arithmetics (MA). After representing RR, RESP and SBP at different time scales through a recently proposed method based on multivariate state space models, the joint information transfer T RESP , SBP → RR is decomposed into unique, redundant and synergistic components, describing the strength of baroreflex modulation independent of respiration ( U SBP → RR ), nonbaroreflex ( U RESP → RR ) and baroreflex-mediated ( R RESP , SBP → RR ) respiratory influences, and simultaneous presence of baroreflex and nonbaroreflex respiratory influences ( S RESP , SBP → RR ), respectively. We find that fast (short time scale) HRV oscillations—respiratory sinus arrhythmia—originate from the coexistence of baroreflex and nonbaroreflex (central) mechanisms at rest, with a stronger baroreflex involvement during HUT. Focusing on slower HRV oscillations, the baroreflex origin is dominant and MA leads to its higher involvement. Respiration influences independent on baroreflex are present at long time scales, and are enhanced during HUT.

The linear and second-order nonlinear optical (NLO) properties of two pyrene-pyridine chromophores, namely, 4-(pyren-1-yl)pyridine (L1) and 4-(2-(pyren-1-yl)ethyl)pyridine (L2), were investigated and modulated by performing protonation/deprotonation cycles or by complexation to d10 metal centers such as Zn(II) and Cu(I) to form the monomeric [Zn(CH3CO2)2(L1)2] complex and the [CuI(L2)]n coordination polymer, respectively. The structures of L1, L2, [Zn(CH3CO2)2(L1)2] and [CuI(L2)]n were determined by means of single-crystal X-ray diffraction studies. The NLO response, measured by the electric-field-induced second harmonic generation (EFISH) technique, was positive for both chromophores and showed an inversion of the sign after exposure to HCl vapors. This process was completely reversible and the original values were restored by simple exposure to NH3 vapors. Coordination of L1 to Zn(II) also resulted in a negative NLO response, although smaller in magnitude compared to the protonated form, due to the weak Lewis acidity of the “Zn(CH3CO2)2” fragment. The results were also interpreted on the basis of DFT/TDDFT calculations.

Chiral polyamidoamino acids were obtained by polyaddition of N,N’-methylenebisacrylamide with d-, d,l- and l-tryptophan (M-d-Trp, M-d,l-Trp and M-l-Trp). l-tryptophan/glycine copolymers, M-G-l-Trp5, M-G-l-Trp10, M-G-l-Trp20 and M-G-l-Trp40, were prepared from l-tryptophan/glycine mixtures. These polymers were amphoteric, with acid-base properties similar to those of the parent amino acids. The l-tryptophan/glycine copolymers with high glycine content were water soluble in the pH range 2-12. M-G-l-Trp40 showed a solubility gap centred at pH 4.5 and all tryptophan homopolymers were soluble only at pH > 7. Dynamic light scattering measurements performed in their solubility ranges, namely 2-11 M-G-l-Trp5, M-G-l-Trp10 and M-G-l-Trp20 and 7-11 for M-G-l-Trp40, M-d-Trp, M-l-Trp and M-d,l-Trp, showed that the size of all samples did not significantly vary with pH. Both M-l-Trp and M-G-l-Trp copolymers showed pH-dependent circular dichroism spectra in the wavelength interval 200–280 nm, revealing structuring. All samples were fluorescent. Their emission spectra were unstructured and, if normalized for their tryptophan content, almost superimposable at the same pH, providing evidence that only tryptophan governed the photoluminescence properties. Changing pH induced in all cases a slight shift of the emission wavelength maximum ascribed to the modification of the microenvironment surrounding the indole ring induced by different protonation degrees.

An analysis of cardiorespiratory dynamics during mental arithmetic, which induces stress, and sustained attention was conducted using information theory. The information storage and internal information of heart rate variability (HRV) were determined respectively as the self-entropy of the tachogram, and the self-entropy of the tachogram conditioned to the knowledge of respiration. The information transfer and cross information from respiration to HRV were assessed as the transfer and cross-entropy, both measures of cardiorespiratory coupling. These information-theoretic measures identified significant nonlinearities in the cardiorespiratory time series. Additionally, it was shown that, although mental stress is related to a reduction in vagal activity, no difference in cardiorespiratory coupling was found when several mental states (rest, mental stress, sustained attention) are compared. However, the self-entropy of HRV conditioned to respiration was very informative to study the predictability of RR interval series during mental tasks, and showed higher predictability during mental arithmetic compared to sustained attention or rest.

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by prevalently redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone.

A challenge for physiologists and neuroscientists is to map information transfer between components of the systems that they study at different scales, in order to derive important knowledge on structure and function from the analysis of the recorded dynamics. The components of physiological networks often interact in a nonlinear way and through mechanisms which are in general not completely known. It is then safer that the method of choice for analyzing these interactions does not rely on any model or assumption on the nature of the data and their interactions. Transfer entropy has emerged as a powerful tool to quantify directed dynamical interactions. In this paper we compare different approaches to evaluate transfer entropy, some of them already proposed, some novel, and present their implementation in a freeware MATLAB toolbox. Applications to simulated and real data are presented.

BACKGROUND: Recent studies have been shown that functional connectivity of cerebral areas is not a static phenomenon, but exhibits spontaneous fluctuations over time. There is evidence that fluctuating connectivity is an intrinsic phenomenon of brain dynamics that persists during anesthesia. Lately, point process analysis applied on functional data has revealed that much of the information regarding brain connectivity is contained in a fraction of critical time points of a resting state dataset. In the present study we want to extend this methodology for the investigation of resting state fMRI spatial pattern changes during propofol-induced modulation of consciousness, with the aim of extracting new insights on brain networks consciousness-dependent fluctuations. METHODS: Resting-state fMRI volumes on 18 healthy subjects were acquired in four clinical states during propofol injection: wakefulness, sedation, unconsciousness, and recovery. The dataset was reduced to a spatio-temporal point process by selecting time points in the Posterior Cingulate Cortex (PCC) at which the signal is higher than a given threshold (i.e., BOLD intensity above 1 standard deviation). Spatial clustering on the PCC time frames extracted was then performed (number of clusters = 8), to obtain 8 different PCC co-activation patterns (CAPs) for each level of consciousness. RESULTS: The current analysis shows that the core of the PCC-CAPs throughout consciousness modulation seems to be preserved. Nonetheless, this methodology enables to differentiate region-specific propofol-induced reductions in PCC-CAPs, some of them already present in the functional connectivity literature (e.g., disconnections of the prefrontal cortex, thalamus, auditory cortex), some others new (e.g., reduced co-activation in motor cortex and visual area). CONCLUSION: In conclusion, our results indicate that the employed methodology can help in improving and refining the characterization of local functional changes in the brain associated to propofol-induced modulation of consciousness.

We implement the Ising model on a structural connectivity matrix describing the brain at two different resolutions. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the the sum of the weights to that node and to the average time between consecutive time flips of spins. The results for the connectome of 66 nodes and for that of 998 nodes are similar, thus suggesting that these properties are scale-independent. Finally, we also find that the brain dynamics at criticality is organized maximally to a rich-club w.r.t. the network of information flows.

BACKGROUND: The left and right amygdalae are key regions distinctly involved in emotion-regulation processes. Individual differences, such as personality features, may affect the implicated neurocircuits. The lateralized amygdala affective processing linked with the temperament dimension Harm Avoidance (HA) remains poorly understood. Resting state functional connectivity imaging (rsFC) may provide more insight into these neuronal processes. METHODS: In 56 drug-naive healthy female subjects, we have examined the relationship between the personality dimension HA on lateralized amygdala rsFC. RESULTS: Across all subjects, left and right amygdalae were connected with distinct regions mainly within the ipsilateral hemisphere. Females scoring higher on HA displayed stronger left amygdala rsFC with ventromedial prefrontal cortical (vmPFC) regions involved in affective disturbances. In high HA scorers, we also observed stronger right amygdala rsFC with the dorsomedial prefrontal cortex (dmPFC), which is implicated in negative affect regulation. CONCLUSIONS: In healthy females, left and right amygdalae seem implicated in distinct mPFC brain networks related to HA and may represent a vulnerability marker for sensitivity to stress and anxiety (disorders).

Whole-cell patch recording is an essential tool for quantitatively establishing the biophysics of brain function, particularly in vivo. This method is of particular interest for studying the functional roles of cortical glial cells in the intact brain, which cannot be assessed with extracellular recordings. Nevertheless, a reasonable success rate remains a challenge because of stability, recording duration and electrical quality constraints, particularly for voltage clamp, dynamic clamp or conductance measurements. To address this, we describe "Touch and Zap", an alternative method for whole-cell patch clamp recordings, with the goal of being simpler, quicker and more gentle to brain tissue than previous approaches. Under current clamp mode with a continuous train of hyperpolarizing current pulses, seal formation is initiated immediately upon cell contact, thus the "Touch". By maintaining the current injection, whole-cell access is spontaneously achieved within seconds from the cell-attached configuration by a self-limited membrane electroporation, or "Zap", as seal resistance increases. We present examples of intrinsic and visual responses of neurons and putative glial cells obtained with the revised method from cat and rat cortices in vivo. Recording parameters and biophysical properties obtained with the Touch and Zap method compare favourably with those obtained with the traditional blind patch approach, demonstrating that the revised approach does not compromise the recorded cell. We find that the method is particularly well-suited for whole-cell patch recordings of cortical glial cells in vivo, targeting a wider population of this cell type than the standard method, with better access resistance. Overall, the gentler Touch and Zap method is promising for studying quantitative functional properties in the intact brain with minimal perturbation of the cell's intrinsic properties and local network. Because the Touch and Zap method is performed semi-automatically, this approach is more reproducible and less dependent on experimenter technique.

A network approach to brain and dynamics opens new perspectives towards understanding of its function. The functional connectivity from functional MRI recordings in humans is widely explored at large scale, and recently also at the voxel level. The networks of dynamical directed connections are far less investigated, in particular at the voxel level. To reconstruct full brain effective connectivity network and study its topological organization, we present a novel approach to multivariate Granger causality which integrates information theory and the architecture of the dynamical network to efficiently select a limited number of variables. The proposed method aggregates conditional information sets according to community organization, allowing to perform Granger causality analysis avoiding redundancy and overfitting even for high-dimensional and short datasets, such as time series from individual voxels in fMRI. We for the first time depicted the voxel-wise hubs of incoming and outgoing information, called Granger causality density (GCD), as a complement to previous repertoire of functional and anatomical connectomes. Analogies with these networks have been presented in most part of default mode network; while differences suggested differences in the specific measure of centrality. Our findings could open the way to a new description of global organization and information influence of brain function. With this approach is thus feasible to study the architecture of directed networks at the voxel level and individuating hubs by investigation of degree, betweenness and clustering coefficient.

We analyze simple dynamical network models which describe the limited capacity of nodes to process the input information. For a proper range of their parameters, the information flow pattern in these models is characterized by exponential distribution of the incoming information and a fat-tailed distribution of the outgoing information, as a signature of the law of diminishing marginal returns. We apply this analysis to effective connectivity networks from human EEG signals, obtained by Granger Causality, which has recently been given an interpretation in the framework of information theory. From the distributions of the incoming versus the outgoing values of the information flow it is evident that the incoming information is exponentially distributed whilst the outgoing information shows a fat tail. This suggests that overall brain effective connectivity networks may also be considered in the light of the law of diminishing marginal returns. Interestingly, this pattern is reproduced locally but with a clear modulation: a topographic analysis has also been made considering the distribution of incoming and outgoing values at each electrode, suggesting a functional role for this phenomenon.

We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy.

Objective:: identify the epidemiological aspects of early fetal and neonatal deaths in children of patients classified with near miss and the factors associated with this outcome., Method:: a cross-sectional study of 79 women identified with near miss and their newborns. The variables were analyzed using Fisher's exact test. Risk factors were estimated based on unadjusted and adjusted odds ratios, and by means of multiple correspondence analysis, with significance for p <0.05., Results:: hypertensive disorders totaled 40.5%; Of these, 58.3% had adverse fetal and neonatal outcome. The newborns admitted to the Neonatal Intensive Care Unit proved to be significant for the outcome (70.8%), gestational age <32 weeks (41.6%), birth weight <2500 (66.7%), neonatal asphyxia (50%) and early respiratory discomfort (72.2%)., Conclusion:: prematurity, neonatal asphyxia, and early respiratory distress were significant characteristics for the outcome among newborns.