Latching dynamics as a basis for short-term recall

We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state—latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.

Latching dynamics as a basis for short-term recall

We discuss simple models for the transient storage in short-term memory of cortical patterns of activity, all based on the notion that their recall exploits the natural tendency of the cortex to hop from state to state – latching dynamics. We show that in one such model, and in simple spatial memory tasks we have given to human subjects, short-term memory can be limited to similar low capacity by interference effects, in tasks terminated by errors, and can exhibit similar sublinear scaling, when errors are overlooked. The same mechanism can drive serial recall if combined with weak order-encoding plasticity. Finally, even when storing randomly correlated patterns of activity the network demonstrates correlation-driven latching waves, which are reflected at the outer extremes of pattern space.

Differential alteration of plant functions by homologous fungal candidate effectors

Rust fungi are plant pathogens that cause epidemics that threaten the production of important plant species, such as wheat, soy, coffee and poplar. Melampsora larici-populina (Mlp) causes the poplar rust and encodes at least 1 184 candidate effectors (CEs), however their functions are poorly known. In this study, we used Arabidopsis plants constitutively expressing CEs of Mlp to discover processes targeted by these fungal proteins. For this purpose, we sequenced the transcriptome and used mass spectrometry to analyse the metabolome of Arabidopsis plants expressing individually one of the 14 selected CEs and of a control line. We found 2 299 deregulated genes across the experiment. Among the down-regulated genes, the KEGG pathways “MAPK signaling pathway” and “Plant-pathogen interaction” were respectively over-represented in six and five of the 14 transgenic lines. Moreover, genes related to hormone response and defense were down-regulated across all transgenic lines are. We further observed that there were 680 metabolites deregulated in at least one CE-expressing transgenic line, with highly unsaturated and phenolic compounds enriched in up-regulated metabolites and peptides enriched among down-regulated metabolites. Interestingly, we found that transgenic lines expressing unrelated CEs had correlated patterns of gene and metabolite deregulation, while expression of CEs belonging to the same family deregulated different genes and metabolites. Taken together, our results indicate that the sequence of effectors and their belonging to families may not be a good predictor of their impact on the plant.ImportanceRust fungi are plant pathogens that threaten the production of important crops, including wheat, soy, coffee and poplar. Effectors are used by pathogens to control the host, however in the case of Melampsora larici-populina, the causal agent of the poplar rust, and other rust fungi these proteins are poorly known. We used Arabidopsis plants expressing candidate effectors (CEs) of Mlp to better understand the interaction between this pathogen and its hosts. We found that expression of unrelated CEs led to similar patterns of gene and metabolite deregulation, while transgenic lines expressing CEs belonging to the same family showed different groups of different genes and metabolites deregulated. Thus, our results suggest that functional annotation of effectors based on sequence similarity may be misleading.

Electrocardiogram stream level correlated patterns as features to classify heartbeats for arrhythmia prediction

PurposeDigital computing and machine learning-driven predictive analysis in the diagnosis of non-communicable diseases are gaining significance. Globally many research studies are focusing on developing comprehensive models for such detection. Categorically in the proposed diagnosis for arrhythmia, which is a critical diagnosis to prevent cardiac-related deaths, any constructive models can be a value proposition. In this study, the focus is on developing a holistic system that predicts the scope of arrhythmia from the given electrocardiogram report. The proposed method is using the sequential patterns of the electrocardiogram elements as features.Design/methodology/approachConsidering the decision accuracy of the contemporary classification methods, which is not adequate to use in clinical practices, this manuscript coined a new dimension of features to perform supervised learning and classification using the AdaBoost classifier. The proposed method has titled “Electrocardiogram stream level correlated patterns as features (ESCPFs),” which takes electrocardiograms (ECGs) signal streams as input records to perform supervised learning-based classification to detect the arrhythmia scope in given ECG record.FindingsFrom the results and comparative reports generated for the study, it is evident that the model is performing with higher accuracy compared to some of the earlier models. However, focusing on the emerging solutions and technologies, if the accuracy factors for the model can be improved, it can lead to compelling predictions and accurate outcome from the process.Originality/valueThe authors represent complete automatic and rapid arrhythmia as classifier, which could be applied online and examine long ECG records sequence efficiently. By releasing the needs for extraction of features, the authors project an application based on raw signals, one result to heart rates date, whose objective is to lessen computation time when attaining minimum classification error outcomes.

MISCELA: discovering simultaneous and time-delayed correlated attribute patterns

This article addresses a new pattern mining problem in time series sensor data, which we call correlated attribute pattern mining. The correlated attribute patterns (CAPs for short) are the sets of attributes (e.g., temperature and traffic volume) on sensors that are spatially close to each other and temporally correlated in their measurements. Although the CAPs are useful to accurately analyze and understand spatio-temporal correlation between attributes, the existing mining methods are inefficient to discover CAPs because they extract unnecessary patterns. Therefore, we propose a mining method Miscela to efficiently discover CAPs. Miscela can discover not only simultaneous correlated patterns but also time delayed correlated patterns. Furthermore, we extend Miscela to automatically search for correlated patterns with any time delays. Through our experiments using three real sensor datasets, we show that the response time of Miscela is up to 20.84 times faster compared with the state-of-the-art method. We show that Miscela discovers meaningful patterns for urban managements and environmental studies.

Application Essays and the Performance of Merit in US Selective Admissions

What sustains the broad conception of merit that organizes academic gatekeeping in the United States? While prior analysts have offered “top-down” explanations of academic leaders expanding criteria of merit to accommodate institutional self-interests, we identify a “bottom-up” mechanism: the application essay. Integrating insights from several strands of sociological theory, we posit that the essays required by schools with selective admissions sustain merit’s breadth by enlisting young people (and their families, teachers, and consultants) in collaborative performances of what might be considered meritorious. Analyzing essay prompts and utilizing human readings and statistical analyses of 220,062 essays contributed by 55,016 applicants to a public US university system, we find circumscribed genre expectations and class-correlated patterns of self-presentation but also substantial range in what the university and its applicants invoke as evidence to support competitive bids for admission. Our work provides a novel explanation of an elaborate but opaque evaluation protocol and surfaces a paradoxically inclusive component of an otherwise fiercely exclusionary evaluative regime.

Decoding the sense of smell: understanding the structural organization within the brain

The olfactory sense is a potent sensory tool which helps us perceive our environment much better. However, smells despite being similar have different impacts on individuals. What makes one odor categorically different from the other and why do people have a unique and personalized experience with smell is an answer that needs to be addressed. In the present article we have discussed the research in which neuroscientists have decoded and described how the relationships between different odors are encoded in the brain. How the brain transforms information about odor chemistry into the perception of smell is a major highlight of this publication. Carefully selected odors with defined molecular structures were delivered in mice and the neural activity was analyzed. It was observed that neuronal representations of smell in the cortex reflected chemical similarities between odors, thus allowing the brain to categorize scents. The study has employed chemo informatics and multiphoton imaging in the mouse to demonstrate both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odor relationships through correlated patterns of activity. The research has given us cues in the direction of how the brain translates odor chemistry into neurochemistry and eventually perception of smell.