Prototypical Patterns Research Articles

Innovation Diffusion Through Generation Cohorts

This paper focuses on the process of innovation diffusion through a population where sets of new members -- called generation cohorts -- enter the population over time. In certain cases where diffusion in a growing population does not follow the prototypical logistic pattern, disaggregating it by generation cohorts makes the diffusion process more apparent. Subsequent generation cohorts experience increased rates of diffusion and higher levels of early adoption. Moreover, the diffusion experienced by the existing population affects the diffusion path of later generation cohorts. After documenting the effect of generation cohorts on diffusion, strategic implications are considered, with incumbents and entrants competing to capture succeeding generation cohorts that each experiences its own path of diffusion.

Development of Suit Prototype Pattern for Mass Customization of Wheelchair Users

Development of Suit Prototype Pattern for Mass Customization of Wheelchair Users

Trajectories of drug use among French young people: Prototypical stages of involvement in illicit drug use

Aims: This study investigated patterns and trajectories of substance use, with a special focus on illicit drugs other than cannabis. It examined both patterns and trajectories of use among a general population-based sample. Methods: We used data from the 2011 French ESCAPAD survey of French 17-year-olds to assess exposure and age of initiation of 14 licit and illicit drugs (N=23,882). Latent class analysis (LCA) and survival analyses were performed. Results: The results of the LCA showed that patterns of illicit drug use clearly distinguished between two groups of other illicit drugs: 1) amphetamines/speed, cocaine, ecstasy/MDMA, magic mushrooms, poppers, and solvents; 2) crack/freebase, GHB/GBL, heroine, LSD, and ketamine. Survival analyses highlighted that trajectories involved the first group before the second one. Conclusions: Prototypical drug use patterns and trajectories should include a distinction between two groups of illicit drugs. Preventive actions should focus on young people in their early teens, since very young users are more likely to progress to illicit drug use, and further studies should include this distinction instead of aggregating other illicit drugs into a single category.

Leukocytes recruited by tumor-derived HMGB1 sustain peritoneal carcinomatosis

ABSTRACTThe factors that determine whether disseminated transformed cells in vivo yield neoplastic lesions have only been partially identified. We established an ad hoc model of peritoneal carcinomatosis by injecting colon carcinoma cells in mice. Tumor cells recruit inflammatory leukocytes, mostly macrophages, and generate neoplastic peritoneal lesions. Phagocyte depletion via clodronate treatment reduces neoplastic growth. Colon carcinoma cells release a prototypic damage-associated molecular pattern (DAMP)/alarmin, High Mobility Group Box1 (HMGB1), which attracts leukocytes. Exogenous HMGB1 accelerates leukocyte recruitment, macrophage infiltration, tumor growth and vascularization. Lentiviral-based HMGB1 knockdown or pharmacological interference with its extracellular impair macrophage recruitment and tumor growth. Our findings provide a preclinical proof of principle that strategies based on preventing HMGB1-driven recruitment of leukocytes could be used for treating peritoneal carcinomatosis.

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems.

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a ‘basin’ of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases.

Abstract A36: Novel therapeutic approaches for human malignant mesothelioma

Abstract Human malignant mesothelioma (MM) is an aggressive and highly lethal cancer that has been linked to asbestos and erionite exposure. MM causes about 3,200 deaths per year in the US and more than 100,000 deaths per year worldwide. We previously found that high-mobility group box-1 protein (HMGB1), a prototypic damage-associated molecular pattern (DAMP), plays a critical role in the early events of HM malignant transformation and MM development. More recently we showed that MM cells are “addicted” to HMGB1 that supports the malignant phenotype by promoting proliferation and invasiveness of MM cells. We demonstrated that MM growth is inhibited by targeting HMGB1 using HMGB1 neutralizing antibody or BoxA, a HMGB1 antagonist. Ethyl pyruvate (EP) is the ethyl ester of pyruvic acid, known to suppress oxygen radical formation and HMGB1 secretion. Here we show that EP impaired motility, survival and proliferation of MM cells in vitro. Both BoxA and EP inhibited the activity of the pro-inflammatory transcription factor NF-kappaB, as well as reduced HMGB1 serum levels and the growth of MM xenografts in SCID mice. Taken together, our results provide a preclinical proof-of-principle that inhibition of HMGB1 activity using either HMGB1 antibody, BoxA or EP offers novel therapeutic approaches for MM treatment. Citation Format: Haining Yang, Sandro Jube, Michele Carbone. Novel therapeutic approaches for human malignant mesothelioma. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy: A New Chapter; December 1-4, 2014; Orlando, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2015;3(10 Suppl):Abstract nr A36.

Problem-Solving Argumentative Patterns in Plenary Debates of the European Parliament

The aim of this paper is to describe the way in which argumentative patterns come into being in plenary debate over legislative issues in the European Parliament. What kind of argumentative patterns are to be expected within this macro context? It is shown that the argumentative patterns that come into being in legislative debate in the European Parliament depend for the most part on the problem-solving argumentation that is put forward in the opening speech by the rapporteur of the parliamentary committee report. This argumentation can be pragmatic problem-solving argumentation or complex problem-solving argumentation. The most important prototypical argumentative patterns are investigated in the argumentation put forward by the Members of parliament. This investigation is based on an inventory of the arguments that can in principle be used to support or attack the initial problem-solving argumentation put forward by the rapporteur.

Prototypical Argumentative Patterns in a Legal Context: The Role of Pragmatic Argumentation in the Justification of Judicial Decisions

In this contribution the prototypical argumentative patterns are discussed in which pragmatic argumentation is used in the context of legal justification in hard cases. First, the function and implementation of pragmatic argumentation in prototypical argumentative patterns in legal justification are addressed. The dialectical function of the different parts of the complex argumentation are explained by characterizing them as argumentative moves that are put forward in reaction to certain forms of critique. Then, on the basis of an exemplary case, the famous Holy Trinity case, the way in which the U.S. Supreme Court uses pragmatic argumentation in this case is discussed by showing how the court instantiates general prototypical argumentative patterns in light of the institutional preconditions of the justification in the context of the specific case.

Can People Benefit From Acute Stress? Social Support, Psychological Improvement, and Resilience After the Virginia Tech Campus Shootings

People’s responses to acute stress are largely thought to comprise four prototypical patterns of resilience, gradual recovery, chronic distress, and delayed distress. Here we present evidence of an additional response pattern: psychological improvement. Female survivors of the Virginia Tech shootings ( N = 368) completed assessments before the shootings and at 2, 6, and 12 months post-shooting. Latent growth mixture modeling revealed distinct trajectories of resilience, chronic distress, delayed distress, continuous distress, and improvement. Although resilience was the most common pattern (56%–59%), a trajectory of substantial improvement in anxiety and depression symptoms also emerged among 13.2% and 7.4% of the sample, respectively. In support of this pattern, improvement was distinctively associated with marked increases in perceived social support and gains in interpersonal resources. Findings suggest a more complex understanding of the impact of mass trauma and a key role for dynamic changes in social support following acute stress.

Understanding Resilience and Other Trajectories of Psychological Distress: a Mixed-Methods Study of Low-Income Mothers Who Survived Hurricane Katrina

Recent longitudinal studies in the aftermath of natural disasters have shown that resilience, defined as a trajectory of consistently low symptoms, is the modal experience, although other trajectories representing adverse responses, including chronic or delayed symptom elevations, occur in a substantial minority of survivors. Although these studies have provided insight into the prototypical patterns of postdisaster mental health, the factors that account for these patterns remain unclear. In the current analysis, we aimed to fill this gap through a mixed-methods study of female participants in the Resilience in Survivors of Katrina (RISK) study. Latent class growth analysis identified six trajectories of psychological distress in the quantitative sample (n=386). Qualitative analysis of in-depth interviews with 54 participants identified predisaster, disaster-related and postdisaster experiences that could account for the trends in the quantitative data. In particular, preexisting and gains in psychosocial resources (e.g., emotion regulation, religiosity) and positive postdisaster impacts (e.g., greater neighborhood satisfaction, improved employment opportunities) were found to underlie resilience and other positive mental health outcomes. Conversely, experiences of childhood trauma, and pre and postdisaster stressors (e.g., difficulties in intimate partner relationships) were common among participants in trajectories representing adverse psychological responses. Illustrative case studies that exemplify each trajectory are presented. The results demonstrate the utility of mixed-methods analysis to provide a richer picture of processes underlying postdisaster mental health.

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

Analysis of gait in rats with olivocerebellar lesions and ability of the nicotinic acetylcholine receptor agonist varenicline to attenuate impairments

Studies have demonstrated that administration of the neuronal nicotinic receptor agonist varenicline to rats with olivocerebellar lesions attenuates balance deficits on a rotorod and balance beam, but the effects of this drug on gait deficits have not been investigated. To accomplish this, male Sprague–Dawley rats were trained to walk on a motorized treadmill at 25 and 35cm/s and baseline performance determined; both temporal and spatial gait parameters were analyzed. A principal component analysis (PCA) was used to identify the key components of gait, and the cumulative gait index (CGI) was calculated, representing deviations from prototypical gait patterns. Subsequently, animals either remained as non-lesioned controls or received injections of 3-acetylpyridine (3-AP)/nicotinamide to destroy the climbing fibers innervating Purkinje cells. The gait of the non-lesioned group was assessed weekly to monitor changes in the normal population, while the gait of the lesioned group was assessed 1week following 3-AP administration, and weekly following the daily administration of saline or varenicline (0.3, 1.0, or 3.0mg free base/kg) for 2 weeks. Non-lesioned animals exhibited a 60–70% increased CGI over time due to increases in temporal gait measures, whereas lesioned animals exhibited a nearly 3-fold increased CGI as a consequence of increases in spatial measures. Following 2 weeks of treatment with the highest dose of varenicline (3.0mg free base/kg), the swing duration of lesioned animals normalized, and stride duration, stride length and step angle in this population did not differ from the non-lesioned population. Thus, varenicline enabled animals to compensate for their impairments and rectify the timing of the gait cycle.

SOFC Anode Based on YSZ Pillars

From the view point of tortuosity, aligned yttria-stabilized zirconia (YSZ) and Ni particles and triple-phase boundaries (TPBs) are promising structures for the low-resistance transportation of species and their chemical reaction in the anode of solid oxide fuel cells (SOFCs). In this study, we formed prototype patterns of YSZ pillars with infiltrated Ni and investigated the effect of the configuration of pillars, corresponding to the TPB length, on the electric-generation performance of full cell including the anode. The YSZ pillars were fabricated using an excimer laser. The pillar width was set to 5, 10, 20, and 40 μm, and deeper trenches were fabricated in wider pillars. The trenches in the YSZ patterns were infiltrated with Ni particles. Finer pillars resulted in higher electric-generation performance. The surface area density of the YSZ pillars was approximately proportional to the maximum power density. This shows that the TPB length density is more important than the transportation resistance of oxygen ions in YSZ pillars. This phenomena is discussed on the basis of simulation by the lattice-Boltzmann method.

Characterization and modeling of anisotropic SL pattern during investment casting process

Photopolymer-based stereo-lithography (SL) rapid prototyping patterns with honeycomb internal structure reduce lead time and material cost for pattern manufacturing and also minimize ceramic mold residue after pattern removal in investment casting process. However, relatively larger thermal expansion of SL pattern tends to cause ceramic shell cracking during the pattern thermal removal. The anisotropy of SL pattern properties incurs different stress states of ceramic shell depending on the orientation of the honeycomb structure. In this work, a comprehensive thermo-mechanical coupled finite element model was developed to characterize the anisotropy of SL pattern and its effect on stress in investment casting shell molds. An experimental-based thermal degradation material model was developed considering anisotropy of the thermo-mechanical properties of SL pattern. The static analysis of slurry dipping and transient thermo-mechanical analysis of the pattern thermal removal process were performed. Both the pattern and ceramic shell behavior were studied and validated with experimental findings. Facesheet thickness/facesheet-to-core ratio and orientation of honeycomb structure were found to be the key factors to shell stress during pattern firing. Decreased facesheet thickness reduces stress in shell but also increases shell stress sensitivity to anisotropy of SL pattern. The developed tool provides an effective and efficient way to evaluate and optimize the structure of SL pattern for investment casting process.

A Bayesian model of category-specific emotional brain responses.

Understanding emotion is critical for a science of healthy and disordered brain function, but the neurophysiological basis of emotional experience is still poorly understood. We analyzed human brain activity patterns from 148 studies of emotion categories (2159 total participants) using a novel hierarchical Bayesian model. The model allowed us to classify which of five categories—fear, anger, disgust, sadness, or happiness—is engaged by a study with 66% accuracy (43-86% across categories). Analyses of the activity patterns encoded in the model revealed that each emotion category is associated with unique, prototypical patterns of activity across multiple brain systems including the cortex, thalamus, amygdala, and other structures. The results indicate that emotion categories are not contained within any one region or system, but are represented as configurations across multiple brain networks. The model provides a precise summary of the prototypical patterns for each emotion category, and demonstrates that a sufficient characterization of emotion categories relies on (a) differential patterns of involvement in neocortical systems that differ between humans and other species, and (b) distinctive patterns of cortical-subcortical interactions. Thus, these findings are incompatible with several contemporary theories of emotion, including those that emphasize emotion-dedicated brain systems and those that propose emotion is localized primarily in subcortical activity. They are consistent with componential and constructionist views, which propose that emotions are differentiated by a combination of perceptual, mnemonic, prospective, and motivational elements. Such brain-based models of emotion provide a foundation for new translational and clinical approaches.

A design proto‐pattern for continuously evaluated forecasting in IBM® InfoSphere® Streams

SummaryDesign patterns relieve much of the difficulty of developing software solutions. As sources and volumes of generated data have increased in recent times, stream computing has emerged as a new paradigm for acquiring and harnessing the data's rich potential to provide insight in near real time. One prominent platform for processing streaming data is IBM® InfoSphere® Streams, but design patterns for the platform are relatively scarce. This article proposes a prototypical application design pattern for continuously evaluated forecasting on the IBM InfoSphere Streams platform. Towards demonstrating the prototypical pattern, the application was implemented in three real‐world scenarios: wind power, sentiment analysis, and stock market data. The purpose of this article is to specify the design proto‐pattern and describe its variants in each different implementation. We believe that the proto‐pattern will be applicable in IBM InfoSphere Streams to any data stream where forecasts over multiple forecast horizons are required. Copyright © 2015 John Wiley & Sons, Ltd.

Patterns of functional vision loss in glaucoma determined with archetypal analysis.

Glaucoma is an optic neuropathy accompanied by vision loss which can be mapped by visual field (VF) testing revealing characteristic patterns related to the retinal nerve fibre layer anatomy. While detailed knowledge about these patterns is important to understand the anatomic and genetic aspects of glaucoma, current classification schemes are typically predominantly derived qualitatively. Here, we classify glaucomatous vision loss quantitatively by statistically learning prototypical patterns on the convex hull of the data space. In contrast to component-based approaches, this method emphasizes distinct aspects of the data and provides patterns that are easier to interpret for clinicians. Based on 13 231 reliable Humphrey VFs from a large clinical glaucoma practice, we identify an optimal solution with 17 glaucomatous vision loss prototypes which fit well with previously described qualitative patterns from a large clinical study. We illustrate relations of our patterns to retinal structure by a previously developed mathematical model. In contrast to the qualitative clinical approaches, our results can serve as a framework to quantify the various subtypes of glaucomatous visual field loss.

Delineating prototypical patterns of substance use initiations over time.

The purpose of this paper is to discover patterns of drug use initiations over time through a multiple event process survival mixture model (MEPSUM model), a novel approach for substance use and prevention research. The MEPSUM model combines survival analysis and mixture modeling-specifically latent class analysis-to examine individual differences in the timing of initiation and cumulative risk of substance use over time, and is applied to cross-sectional survey data on drug initiations. Data are drawn from the 2009 National Survey on Drug Use and Health. The survey includes responses from 55 772 individuals (52.05% female). The age of first use of nine different types of substances are examined, including alcohol, tobacco, cocaine and non-medical use of prescription drugs. It is argued that six patterns parsimoniously describe the population's risk of initiating different substances over time, described colloquially as general abstainers; early, late and progressive soft drug users; and early and late hard drug users. Both gender and ethnicity significantly predict the patterns, with Caucasians and males having a higher risk for the hard drug-using patterns. The MEPSUM model produced stable results in this application, as the patterns are validated in a split-sample design. The MEPSUM model provides a statistical framework from which to evaluate patterns of risk for drug initiations over time and predict substance use trajectories relevant to public health interventions. The patterns that result from the model can be used as outcomes for subsequent investigations of etiological and mediating mechanisms.

Topological sparse learning of dynamic form patterns.

Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches.

Data-Driven Rule Mining and Representation of Temporal Patterns in Physiological Sensor Data.

Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, …) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.