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Introduction Patients suffering from cardiogenic shock (CS) and mitral regurgitation (MR) demonstrate worse prognosis, with higher mortality rates. We sought to evaluate the effectiveness of urgent valve intervention of the mitral valve, using transcatheter edge-to-edge repair (TEER) procedures in patients presenting with CS in a tertiary Intensive Coronary Care Unit (ICCU). Methods and Results Patients with unremitting CS and severe MR were selected for urgent TEER. Baseline clinical and echocardiographic characteristics were recorded, as well as procedural success (MR severity and hemodynamics), and 30-days and 6-month mortality. Urgent TEER was done in 13 patients, whose average age was 70 years; 12 (92%) of the patients were male. All 13 patients had suffered previous ischemic heart disease—12 (92%) with either acute severe MR or worsening of previously known MR by an acute ischemic event. Using the SCAI criteria, 8 patients (61%) were classified as ‘E’ (Extreme) category; 4 (31%) were classified as ‘C’. At 30 days, 12 out of the 13 patients survived (corresponding to an 8% mortality rate); all of those 12 patients remained alive at 6 months post-admission/procedure. Conclusions The use of TEER was associated with greater 30-day and 6-month survival rates, compared to the worldwide mortality rates of patients admitted with CS. This finding may change the previous paradigm that CS and MR are associated with the worst outcome, and we might be able to offer these patients a safe and effective therapeutic option.

Empirical studies show that epidemiological models based on an epidemic’s initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population’s susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic’s initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.

Epidemiological models for the spread of pathogens in a population are usually only able to describe a single pathogen. This makes their application unrealistic in cases where multiple pathogens with similar symptoms are spreading concurrently within the same population. Here we describe a method which makes possible the application of multiple single-strain models under minimal conditions. As such, our method provides a bridge between theoretical models of epidemiology and data-driven approaches for modeling of influenza and other similar viruses. Our model extends the Susceptible–Infected–Recovered model to higher dimensions, allowing the modeling of a population infected by multiple viruses. We further provide a method, based on an overcomplete dictionary of feasible realizations of SIR solutions, to blindly partition the time series representing the number of infected people in a population into individual components, each representing the effect of a single pathogen. We demonstrate the applicability of our proposed method on five years of seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We demonstrate that our method describes, on average, 44% of the variance in the ILI time series. The individual infectious components derived from our model are matched to known viral profiles in the populations, which we demonstrate matches that of independently collected epidemiological data. We further show that the basic reproductive numbers ( R 0 ) of the matched components are in range known for these pathogens. Our results suggest that the proposed method can be applied to other pathogens and geographies, providing a simple method for estimating the parameters of epidemics in a population.

Computer malware and biological pathogens often use similar mechanisms of infections. For this reason, it has been suggested to model malware spread using epidemiological models developed to characterize the spread of biological pathogens. However, most work examining the similarities between malware and pathogens using such methods was based on theoretical analysis and simulation. Here we extend the classical Susceptible-Infected-Recovered (SIR) epidemiological model to describe two of the most common infection methods used by malware. We fit the proposed model to malware collected over a period of one year from a major anti-malware vendor. We show that by fitting the proposed model it is possible to identify the method of transmission used by the malware, its rate of infection, and the number of machines which will be infected unless blocked by anti-virus software. The Spearman correlation between the number of actual and predicted infected machines is 0.84. Examining cases where an anti-malware "signature" was transmitted to susceptible computers by the anti-virus provider, we show that the time to remove the malware will be short and independent of the number of infected computers if fewer than approximately 60% of susceptible computers have been infected. If more computers were infected, the time to removal will be approximately 3.2 greater, and will depend on the fraction of infected computers. Our results show that the application of epidemiological models of infection to malware can provide anti-virus providers with information on malware spread and its potential damage. We further propose that similarities between computer malware and biological pathogens, the availability of data on the former and the dearth of data on the latter, make malware an extremely useful model for testing interventions which could later be applied to improve medicine., Comment: 9 pages, 2 figure, 2 tables

Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we present a tensor-driven multi-compartment version of the classic Susceptible–Infected–Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. We develop an algorithm to estimate the model’s parameters in a high dimensional setting. The model and the algorithm are used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV), independently. We fit the data with correlations of R 2 = 0 . 70 , and 0.52 for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27%–30% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent season can be predicted using the model parameters estimated for previous seasons and as few as 7 weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season.

Recently, it has been shown that there exists a temporal synaptic learning curve, composed of potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occurring in the opposite order. We investigate the effect that such a curve can have on the temporal realization of an attractor in an associative memory network of spiking neurons. We find that an interesting result of this learning paradigm is the formation of distributed synchrony, i.e. spontaneous division of the Hebbian cell assembly into groups of cells that fire in a cyclic manner.

A novel computational model of a pre-attentive system performing visual-search is presented. The model processes various types of displays, reproduced from three sources of visual-search experimental data: Duncan and Humphreys, Psychol. Rev. 96 (1989) 453–458, Treisman and Sato, J. Exp. Psuchol. 16 (1990) 459–478, and Wolfe, Friedman-Hill, Stewart, O'Connell, J. Exp. Psychol. 18 (1992) 34–49. The response–time-slopes measured in these experiments suggest that some of the displays are searched serially while others are scanned in parallel. Our model operates in two phases. First, the visual-search displays are compressed to overcome assumed biological capacity limitations. Compression is achieved by projecting the tasks’ displays on a small set of feature maps. These features have been extracted from a large set of natural images by means of principal component analysis. Second, the compressed representations are further processed to identify a target in the display. The model succeeds in fast detection of targets in experimentally labeled parallel displays, but fails with serial ones. Analysis of the compressed representations reveals that compressed parallel displays contain global information that enables instantaneous target detection. However, in serial displays’ representations, this global information is obscure and hence, a target detection system should resort to a serial, attentional scan of local features across the display. Our analysis provides a numerical criterion that is strongly correlated with the experimental response–time-slopes. It also provides new insight to the mechanisms of visual-attention, suggesting a self-organized representation of Treisman's feature maps, which may be implemented in other paradigms in the field.

Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multimodular associative memory network, whose functional goal is to store patterns with different coding levels—patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, synaptic inputs should be segregated into intramodular projections and intermodular projections, with the latter undergoing additional nonlinear dendritic processing. This segregation makes sense anatomically if the intermodular projections represent distal synaptic connections on apical dendrites. It is then straightforward to show that memories encoded in more modules are more resilient to focal afferent damage. Further hierarchical segregation of intermodular connections on the dendritic tree improves this resilience, allowing memory retrieval from input to just one of the modules in which it is encoded.

Recent imaging studies testify that objects are stored in the brain in a distributed network of discrete cortical areas. Motivated by these observations, we study a multi-modular associative memory network, whose functional goal is to store patterns of different coding levels, i.e., patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, the synaptic inputs to each neuron should be segregated to intramodular projections whose activations are summed up linearly, and inter-modular projections whose activations are summed up in a nonlinear fashion. This may reflect dendritic processing of distal synaptic connections. Moreover, we find that further hierarchical segregation of inter-modular connections on the dendritic tree improves memory retrieval from partial input cues, and improves resilience of the network to afferent damage.

In the framework of an associative memory model, we study the interplay between synaptic deletion and compensation, and memory deterioration, a clinical hallmark of Alzheimer's disease. Our study is motivated by experimental evidence that there are regulatory mechanisms that take part in the homeostasis of neuronal activity and act on the neuronal level. We show that following synaptic deletion, synaptic compensation can be carried out efficiently by a local, dynamic mechanism, where each neuron maintains the profile of its incoming post-synaptic current. Our results open up the possibility that the primary factor in the pathogenesis of cognitive deficiencies in Alzheimer's disease (AD) is the failure of local neuronal regulatory mechanisms. Allowing for neuronal death, we observe two pathological routes in AD, leading to different correlations between the levels of structural damage and functional decline.

A recent study claimed to have obtained evidence that participants can solve invisible multistep arithmetic equations (Sklar et al., 2012). The authors used a priming paradigm in which reaction times to targets congruent with the equation's solution were responded to faster compared with incongruent ones. We critically reanalyzed the data set of Sklar et al. and show that the claims being made in the article are not fully supported by the alternative analyses that we applied. A Bayesian reanalysis of the data accounting for the random variability of the target stimuli in addition to the subjects shows that the evidence for priming effects is less strong than initially claimed. That is, although Bayes factors revealed evidence for the presence of a priming effect, it was generally weak. Second, the claim that unconscious arithmetic occurs for subtraction but not for addition is not supported when the critical interaction is tested. Third, the data do not show well-established features of numerosity priming as derived from V-shaped response time curves for prime-target distances. Fourth, we show that it is impossible to classify reaction times as resulting from congruent or incongruent prime-target relationships, which should be expected if their results imply that participants genuinely solve the equations on each trial. We conclude that the claims being made in the original article are not fully supported by the analyses that we apply. Together with a recent failure to replicate the original results and a critique of the analysis based on regression to the mean, we argue that the current evidence for unconscious arithmetic is inconclusive. We argue that strong claims require strong evidence and stress that cumulative research strategies are needed to provide such evidence.

We investigate the formation of a Hebbian cell assembly of spiking neurons, using a temporal synaptic learning curve that is based on recent experimental findings. It includes potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occurring in the reverse order. The coupling between the dynamics of synaptic learning and that of neuronal activation leads to interesting results. One possible mode of activity is distributed synchrony, implying spontaneous division of the Hebbian cell assembly into groups, or subassemblies, of cells that fire in a cyclic manner. The behavior of distributed synchrony is investigated both by simulations and by analytic calculations of the resulting synaptic distributions.

Publisher Summary This chapter describes a model, in which the network is constructed of different modules, assuming each module to represent a different cortical area. There exists evidence for cortical modules in the somatosensory, visual, and association cortices. Giving a multi-modular structure, it is easy to store the memory patterns that are coded in a variable number of modules. This problem is introduced and found that they have to separately define the category to which each memory belongs, thus determining its level of modular coding, and then to modulate synapses and neuronal thresholds during retrieval. This process has led to conclusion that storage and retrieval of memories that are coded in a variable number of modules must involve consciousness. The chapter also introduces a low-level mechanism that solves the problem and based on a functional distinction between intra-modular and inter-modular synaptic couplings.

Since their conception half a century ago, Hebbian cell assemblies have become a basic term in the neurosciences, and the idea that learning takes place through synaptic modifications has been accepted as a fundamental paradigm. As synapses undergo continuous metabolic turnover, adopting the stance that memories are engraved in the synaptic matrix raises a fundamental problem: How can memories be maintained for very long time periods? We present a novel solution to this long-standing question, based on biological evidence of neuronal regulation mechanisms that act to maintain neuronal activity. Our mechanism is developed within the framework of a neural model of associative memory. It is operative in conjunction with random activation of the memory system and is able to counterbalance degradation of synaptic weights and normalize the basins of attraction of all memories. Over long time periods, when the variance of the degradation process becomes important, the memory system stabilizes if its synapses are appropriately bounded. Thus, the remnant memory system is obtained by a dynamic process of synaptic selection and growth driven by neuronal regulatory mechanisms. Our model is a specific realization of dynamic stabilization of neural circuitry, which is often assumed to take place during sleep.