Context-dependent Information Research Articles

Unique transcriptional signatures correlate with behavioral and psychological symptom domains in Alzheimer’s disease

Despite the significant burden, cost, and worse prognosis of Alzheimer’s disease (AD) with behavioral and psychological symptoms of dementia (BPSD), little is known about the molecular causes of these symptoms. Using antemortem assessments of BPSD in AD, we demonstrate that individual BPSD can be grouped into 4 domain factors in our cohort: affective, apathy, agitation, and psychosis. Then, we performed a transcriptome-wide analysis for each domain utilizing bulk RNA-seq of post-mortem anterior cingulate cortex (ACC) tissues. Though all 4 domains are associated with a predominantly downregulated pattern of hundreds of differentially expressed genes (DEGs), most DEGs are unique to each domain, with only 22 DEGs being common to all BPSD domains, including TIMP1. Weighted gene co-expression network analysis (WGCNA) yielded multiple transcriptional modules that were shared between BPSD domains or unique to each domain, and NetDecoder was used to analyze context-dependent information flow through the biological network. For the agitation domain, we found that all DEGs and a highly associated transcriptional module were functionally enriched for ECM-related genes including TIMP1, TAGLN, and FLNA. Another unique transcriptional module also associated with the agitation domain was enriched with genes involved in post-synaptic signaling, including DRD1, PDE1B, CAMK4, and GABRA4. By comparing context-dependent changes in DEGs between cases and control networks, ESR1 and PARK2 were implicated as two high-impact genes associated with agitation that mediated significant information flow through the biological network. Overall, our work establishes unique targets for future study of the biological mechanisms of BPSD and resultant drug development.

Optimizing Timely Coverage in Communication Constrained Collaborative Sensing Systems

We consider a collection of distributed sensor nodes periodically exchanging information to achieve real-time situational awareness in a communication constrained setting, e.g., collaborative sensing amongst vehicles to improve safety-critical decisions. Nodes may be both consumers and producers of sensed information. Consumers express interest in information about particular locations, e.g., obstructed regions and/or road intersections, whilst producers broadcast updates on what they are currently able to see. Accordingly, we introduce and explore optimizing trade-offs between the coverage and the space-time interest weighted average “age” of the information available to consumers. We consider two settings that capture the fundamental character of the problem. The first addresses selecting a subset of producers that maximizes the coverage of the consumers preferred regions and minimizes the average age of these regions given that producers provide updates at a fixed rate. The second addresses the minimization of the interest weighted average age achieved by a fixed subset of producers with possibly overlapping coverage by optimizing their update rates. The first problem is shown to be submodular and thus amenable to greedy optimization while the second has a non-convex/non-concave cost function which is amenable to effective optimization using the Frank-Wolfe algorithm. Numerical results exhibit the benefits of context dependent optimization information sharing among obstructed sensing nodes.

Response-adaptive randomization for multiarm clinical trials using context-dependent information measures.

Theoretical-information approach applied to the clinical trial designs appeared to bring several advantages when tackling a problem of finding a balance between power and expected number of successes (ENS). In particular, it was shown that the built-in parameter of the weight function allows finding the desired trade-off between the statistical power and number of treated patients in the context of small population PhaseII clinical trials. However, in real clinical trials, randomized designs are more preferable. The goal of this research is to introduce randomization to a deterministic entropy-based sequential trial procedure generalized to multiarm setting. Several methods of randomization applied to an entropy-based design are investigated in terms of statistical power and ENS. Namely, the four design types are considered: (a) deterministic procedures, (b) naive randomization using the inverse of entropy criteria as weights, (c) block randomization, and (d) randomized penalty parameter. The randomized entropy-based designs are compared to randomized Gittins index (GI) and fixed randomization (FR). After the comprehensive simulation study, the following conclusion on block randomization is made: for both entropy-based and GI-based block randomization designs the degree of randomization induced by forward-looking procedures is insufficient to achieve a decent statistical power. Therefore, we propose an adjustment for the forward-looking procedure that improves power with almost no cost in terms of ENS. In addition, the properties of randomization procedures based on randomly drawn penalty parameter are also thoroughlyinvestigated.

Inferred Attractiveness: A generalized mechanism for sexual selection that can maintain variation in traits and preferences over time.

Sexual selection by mate choice is a powerful force that can lead to evolutionary change, and models of why females choose particular mates are central to understanding its effects. Predominant mate choice theories assume preferences are determined solely by genetic inheritance, an assumption still lacking widespread support. Moreover, preferences often vary among individuals or populations, fail to correspond with conspicuous male traits, or change with context, patterns not predicted by dominant models. Here, we propose a new model that explains this mate choice complexity with one general hypothesized mechanism, "Inferred Attractiveness." In this model, females acquire mating preferences by observing others' choices and use context-dependent information to infer which traits are attractive. They learn to prefer the feature of a chosen male that most distinguishes him from other available males. Over generations, this process produces repeated population-level switches in preference and maintains male trait variation. When viability selection is strong, Inferred Attractiveness produces population-wide adaptive preferences superficially resembling "good genes." However, it results in widespread preference variation or nonadaptive preferences under other predictable circumstances. By casting the female brain as the central selective agent, Inferred Attractiveness captures novel and dynamic aspects of sexual selection and reconciles inconsistencies between mate choice theory and observed behavior.

Network analysis reveals context-dependent structural complexity of social calls in serrate-legged small treefrogs.

Vocal communication plays an important role in survival, reproduction, and animal social association. Birds and mammals produce complex vocal sequence to convey context-dependent information. Vocalizations are conspicuous features of the behavior of most anuran species (frogs and toads), and males usually alter their calling strategies according to ecological context to improve the attractiveness/competitiveness. However, very few studies have focused on the variation of vocal sequence in anurans. In the present study, we used both conventional method and network analysis to investigate the context-dependent vocal repertoire, vocal sequence, and call network structure in serrate-legged small treefrogs Kurixalus odontotarsus. We found that male K. odontotarsus modified their vocal sequence by switching to different call types and increasing repertoire size in the presence of a competitive rival. Specifically, compared with before and after the playback of advertisement calls, males emitted fewer advertisement calls, but more aggressive calls, encounter calls, and compound calls during the playback period. Network analysis revealed that the mean degree, mean closeness, and mean betweenness of the call networks significantly decreased during the playback period, which resulted in lower connectivity. In addition, the increased proportion of one-way motifs and average path length also indicated that the connectivity of the call network decreased in competitive context. However, the vocal sequence of K. odontotarsus did not display a clear small-world network structure, regardless of context. Our study presents a paradigm to apply network analysis to vocal sequence in anurans and has important implications for understanding the evolution and function of sequence patterns.

Restraint Practices in Incapable Wandering Patients During COVID-19: Ethics and Best Practice Recommendations.

Patients who wander as one of their psychological and behavioural symptoms of dementia are often unable to follow or recall Infection Prevention and Control precautions, putting them at risk of contracting or spreading COVID-19. Physical and chemical restraints have been used to limit the risk of transmission to wandering patients and their care providers, but restraints are not the standard of care for wandering behaviour in non-pandemic scenarios. Although provincial policies on restraint use are available, their guidance may not provide the context-dependent information necessary for individual patient decisions. To address this knowledge gap, we reviewed the medical, ethical, and legal considerations through an interdisciplinary approach including nurses, physicians, ethicists, hospital leadership, risk management, and legal counsel. We present an ethical framework that front-line health-care workers can use to create a balanced patient-centred care plan for incapable wandering patients who are at risk of contracting or spreading COVID-19.

Daily Functional Activities in Context After Stroke: A Large-Scale Ecological Momentary Assessment (EMA) Study

<h3>Research Objectives</h3> To describe where, with whom, and how time was spent daily, to characterize these daily activity patterns as a function of employment, mobility device use, and cognitive functioning, and to validate EMA methods of daily activities and symptoms in people with stroke. <h3>Design</h3> Longitudinal observational study using EMA to assess real-time daily functional activities in natural contexts. <h3>Setting</h3> Home and community. <h3>Participants</h3> 212 participants with mild-to-moderate chronic stroke (55% males, 90% ischemic stroke; median age = 60 years). <h3>Interventions</h3> Not applicable. <h3>Main Outcome Measures</h3> Participants responded to a smartphone app five times daily for two weeks. The app queried activities, environmental contexts, and concurrent post-stroke symptoms. Participants also completed standardized assessments of daily activities and symptoms during lab visits. <h3>Results</h3> Participants spent the most time at home. They participated most in passive, unproductive activities (27%) but least in physical activities (4%). Unemployed individuals reported fewer vocational activities but more activities of daily living (ADL) and passive activities than employed counterparts. Mobility device users and those with cognitive problems spent less time on vocational activities and more on ADL than non-users and those without cognitive problems. Our data showed small-to-moderate correlations of EMA with in-lab measures of daily functioning (r=-0.30 to 0.35, p-value < 0.05) and large correlations between EMA and in-lab measures of symptoms (r= -0.64 to 0.79, p-value < 0.0001). <h3>Conclusions</h3> EMA can provide context-dependent information of daily functional activities in people after stroke. Clinicians may consider using EMA to examine post-stroke functional recovery and monitor patients for home- and community-based programs. <h3>Author(s) Disclosures</h3> None disclosed.

The Study on the Text Classification Based on Graph Convolutional Network and BiLSTM

Graph Convolutional Neural Network (GCN) is widely used in text classification tasks. Furthermore, it has been effectively used to accomplish tasks that are thought to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and it is not good at capturing local information. The Bidirectional Encoder Representation from Transformers (BERT) has the ability to capture contextual information in sentences or documents, but it is limited in capturing global (the corpus) information about vocabulary in a language, which is the advantage of GCN. Therefore, this paper proposes an improved model to solve the above problems. The original GCN uses word co-occurrence relationships to build text graphs. Word connections are not abundant enough and cannot capture context dependencies well, so we introduce a semantic dictionary and dependencies. While the model enhances the ability to capture contextual dependencies, it lacks the ability to capture sequences. Therefore, we introduced BERT and Bi-directional Long Short-Term Memory (BiLSTM) Network to perform deeper learning on the features of text, thereby improving the classification effect of the model. The experimental results show that our model is more effective than previous research reports on four text classification datasets.

Enhancing Attention’s Explanation Using Interpretable Tsetlin Machine

Explainability is one of the key factors in Natural Language Processing (NLP) specially for legal documents, medical diagnosis, and clinical text. Attention mechanism has been a popular choice for such explainability recently by estimating the relative importance of input units. Recent research has revealed, however, that such processes tend to misidentify irrelevant input units when explaining them. This is due to the fact that language representation layers are initialized by pre-trained word embedding that is not context-dependent. Such a lack of context-dependent knowledge in the initial layer makes it difficult for the model to concentrate on the important aspects of input. Usually, this does not impact the performance of the model, but the explainability differs from human understanding. Hence, in this paper, we propose an ensemble method to use logic-based information from the Tsetlin Machine to embed it into the initial representation layer in the neural network to enhance the model in terms of explainability. We obtain the global clause score for each word in the vocabulary and feed it into the neural network layer as context-dependent information. Our experiments show that the ensemble method enhances the explainability of the attention layer without sacrificing any performance of the model and even outperforming in some datasets.

Limitations to coral recovery along an environmental stress gradient.

Positive feedbacks driving habitat-forming species recovery and population growth are often lost as ecosystems degrade. For such systems, identifying mechanisms that limit the re-establishment of critical positive feedbacks is key to facilitating recovery. Theory predicts the primary drivers limiting system recovery shift from biological to physical as abiotic stress increases, but recent work has demonstrated that this seldom happens. We combined field and laboratory experiments to identify variation in limitations to coral recovery along an environmental stress gradient at Ningaloo Reef and Exmouth Gulf in northwest Australia. Many reefs in the region are coral depauperate due to recent cyclones and thermal stress. In general, recovery trajectories are prolonged due to limited coral recruitment. Consistent with theory, clearer water reefs under low thermal stress appear limited by biological interactions: competition with turf algae caused high mortality of newly settled corals and upright macroalgal stands drove mortality in transplanted juvenile corals. Laboratory experiments showed a positive relationship between crustose coralline algae cover and coral settlement, but only in the absence of sedimentation. Contrary to expectation, coral recovery does not appear limited by the survival or growth of recruits on turbid reefs under higher thermal stress, but to exceptionally low larval supply. Laboratory experiments showed that larval survival and settlement are unaffected by seawater quality across the study region. Rather, connectivity models predicted that many of the more turbid reefs in the Gulf are predominantly self seeded, receiving limited supply under degraded reef states. Overall, we find that the influence of oceanography can overwhelm the influences of physical and biological interactions on recovery potential at locations where environmental stressors are high, whereas populations in relatively benign physical conditions are predominantly structured by local ecological drivers. Such context-dependent information can help guide expectations and assist managers in optimizing strategies for spatial conservation planning for system recovery.

Behavioural context shapes vocal sequences in two anuran species with different repertoire sizes

Acoustic signals in animals serve to convey context-dependent information to receivers. Birds and mammals combine diverse sounds into complex sequences to communicate, but the role of temporal sequencing of signals remains understudied in other taxa. Anuran vocalizations are a prominent feature of their life history, and function in defence of territories and to attract mates. However, there are few data on whether anurans pattern their calls into sequences, and whether temporal sequences convey information about context. Here, we investigated the context-dependent vocal repertoire and the use of vocal sequences by two anuran species belonging to different lineages, comparing frogs vocalizing alone and in the presence of a territorial rival. Using a robust analytical framework, we present evidence that both species modify their vocal sequence structure according to context. Specifically, one species (with a smaller repertoire, from a more basal lineage) appends notes to generate more complex sequences, whereas the other (more recently diverged and with a larger repertoire) shifts to different note types, resulting in different sequences for different contexts. Thus, despite differences in repertoire size, both frog species are capable of adjusting the temporal sequence of vocalizations to communicate in different contexts. Vocal sequences and context-dependent ‘syntax’ may be more common in anurans than previously thought, and our methodology presents a paradigm to study the evolution and function of these complex vocal patterns.

Weak central coherence - construct conception, development, research methods.

Central coherence allows for integration of different stimuli into a coherent whole. It also enables context-dependent information processing. This term was coined in an effort to arrange multiple observations of cognitive functioning of people with autism spectrum disorder (ASD). Weak central coherence, which is characteristic of ASD, is understood as adivergent cognitive style with a tendency to process incoming information locally. This allows us to describe central coherence as a continuous trait. Despite manifold research programs, a conclusive conceptualization of the construct is still lacking. The open question is whether weak central coherence constitutes alimited ability to integrate stimuli or rather an increased ability to focus on details. An important postulate concerns the establishment of a standard for measuring central coherence and its individual dimensions. Studies of central coherence outside of ASD context are scarce and deal predominantly with eating disorders. The following work is an overview of the current state of knowledge about central coherence construct and methods of research.

An Integration Model for Text Classification using Graph Convolutional Network and BERT

Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.

New perspectives and results for Smart Operators in industry 4.0: A human-centered approach

Digital solutions (including Extended Reality as well as web, mobile and AI technologies), among others, are entering a broad variety of industries and modifying the capabilities of operators through (close to) real-time display of context-dependent information. However, little is known with respect to two major issues: (i) how these technologies can be seamlessly integrated for product/process and overall manufacturing system management providing of syntactic, semantic and functional interoperability and reusability among the different manufacturing systems departments; (ii) how they can be conveyed to operators also taking into account the cognitive load that is incurred by the operators.To this end, starting from a previous article (Longo et al., 2017), this article designs and proposes the KNOW4I approach and its practical implementation in an ICT platform (the KNOW4I platform) to further empower the Smart Operators concept.Two major objectives are pursued. The former is to set a standard referred to as the KNOW4I methodological approach for knowledge representation, knowledge management and digital contents management within the Smart Operator domain. The latter is the implementation of the aforementioned approach as part of the KNOW4I platform that includes a suite of Smart Utilities and Objects intelligently and interactively linked with a newly released version of the Sophos-MS digital and intelligent Assistant. Experimentations and results based on multiple KPIs are carried out to account for the effectiveness of the proposed framework.

Memory-Based Semantic Parsing

Abstract We present a memory-based model for context- dependent semantic parsing. Previous approaches focus on enabling the decoder to copy or modify the parse from the previous utterance, assuming there is a dependency between the current and previous parses. In this work, we propose to represent contextual information using an external memory. We learn a context memory controller that manages the memory by maintaining the cumulative meaning of sequential user utterances. We evaluate our approach on three semantic parsing benchmarks. Experimental results show that our model can better process context-dependent information and demonstrates improved performance without using task-specific decoders.

Performance-based Social Comparisons in Humans and Long-tailed Macaques

Social comparisons are a fundamental feature of human thinking and affect self-evaluations and task performance. Little is known about the evolutionary origins of social comparison processes, however. Previous studies that investigated performance-based social comparisons in nonhuman primates yielded mixed results. We report three experiments that aimed (a) to explore how the task type may contribute to performance in monkeys, and (b) how a competitive set-up affects monkeys compared to humans. In a co-action touchscreen task, monkeys were neither influenced by nor interested in the performance of the partner. This may indicate that the experimental set-up was not sufficiently relevant to trigger social comparisons. In a novel co-action foraging task, monkeys increased their feeding speed in competitive and co-active conditions, but not in relation to the degree of competition. In an analogue of the foraging task, human participants were affected by partner performance and experimental context, indicating that the task is suitable to elicit social comparisons in humans. Our studies indicate that specifics of task and experimental setting are relevant to draw the monkeys’ attention to a co-actor and that, in line with previous research, a competitive element was crucial. We highlight the need to explore what constitutes “relevant” social comparison situations for monkeys as well as nonhuman animals in general, and point out factors that we think are crucial in this respect (e.g., task type, physical closeness, and the species’ ecology). We discuss that early forms of social comparisons evolved in purely competitive environments with increasing social tolerance and cooperative motivations allowing for more fine-grained processing of social information. Competition driven effects on task performance might constitute the foundation for the more elaborate social comparison processes found in humans, which may involve context-dependent information processing and metacognitive monitoring.

Reward motivation increases univariate activity but has limited effect on coding of task-relevant information across the frontoparietal cortex

Selection and integration of information based on current goals is fundamental for goal-directed behavior. Reward motivation has been shown to improve behavioral performance, yet the neural mechanisms that link motivation and control processes, and in particular its effect on context-dependent information processing, remain unclear. We used functional magnetic resonance imaging (fMRI) in 24 human volunteers (13 females) to test whether reward motivation enhances the coding of task-relevant information across the frontoparietal cortex, as would be predicted based on previous experimental evidence and theoretical accounts. In a cued target detection task, participants detected whether an object from a cued visual category was present in a subsequent display. The combination of the cue and the object visual category determined the behavioral status of the objects. To manipulate reward motivation, half of all trials offered the possibility of a monetary reward. We observed an increase with reward in overall univariate activity across the frontoparietal control network when the cue and subsequent object were presented. Multivariate pattern analysis (MVPA) showed that behavioral status information for the objects was conveyed across the network. However, in contrast to our prediction, reward did not increase the discrimination between behavioral status conditions in the stimulus epoch of a trial when object information was processed depending on a current context. In the high-level general-object visual region, the lateral occipital complex, the representation of behavioral status was driven by visual differences and was not modulated by reward. Our study provides useful evidence for the limited effects of reward motivation on task-related neural representations and highlights the necessity to unravel the diverse forms and extent of these effects.

Neuropeptides as Primary Mediators of Brain Circuit Connectivity.

Across sleep and wakefulness, brain function requires inter-neuronal interactions lasting beyond seconds. Yet, most studies of neural circuit connectivity focus on millisecond-scale interactions mediated by the classic fast transmitters, GABA and glutamate. In contrast, neural circuit roles of the largest transmitter family in the brain–the slow-acting peptide transmitters–remain relatively overlooked, or described as “modulatory.” Neuropeptides may efficiently implement sustained neural circuit connectivity, since they are not rapidly removed from the extracellular space, and their prolonged action does not require continuous presynaptic firing. From this perspective, we review actions of evolutionarily-conserved neuropeptides made by brain-wide-projecting hypothalamic neurons, focusing on lateral hypothalamus (LH) neuropeptides essential for stable consciousness: the orexins/hypocretins. Action potential-dependent orexin release inside and outside the hypothalamus evokes slow postsynaptic excitation. This excitation does not arise from modulation of classic neurotransmission, but involves direct action of orexins on their specific G-protein coupled receptors (GPCRs) coupled to ion channels. While millisecond-scale, GABA/glutamate connectivity within the LH may not be strong, re-assessing LH microcircuits from the peptidergic viewpoint is consistent with slow local microcircuits. The sustained actions of neuropeptides on neuronal membrane potential may enable core brain functions, such as temporal integration and the creation of lasting permissive signals that act as “eligibility traces” for context-dependent information routing and plasticity. The slowness of neuropeptides has unique advantages for efficient neuronal processing and feedback control of consciousness.

Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures

In many rare disease Phase II clinical trials, two objectives are of interest to an investigator: maximising the statistical power and maximising the number of patients responding to the treatment. These two objectives are competing, therefore, clinical trial designs offering a balance between them are needed. Recently, it was argued that response-adaptive designs such as families of multi-arm bandit (MAB) methods could provide the means for achieving this balance. Furthermore, response-adaptive designs based on a concept of context-dependent (weighted) information criteria were recently proposed with a focus on Shannon’s differential entropy. The information-theoretic designs based on the weighted Renyi, Tsallis and Fisher informations are also proposed. Due to built-in parameters of these novel designs, the balance between the statistical power and the number of patients that respond to the treatment can be tuned explicitly. The asymptotic properties of these measures are studied in order to construct intuitive criteria for arm selection. A comprehensive simulation study shows that using the exact criteria over asymptotic ones or using information measures with more parameters, namely Renyi and Tsallis entropies, brings no sufficient gain in terms of the power or proportion of patients allocated to superior treatments. The proposed designs based on information-theoretical criteria are compared to several alternative approaches. For example, via tuning of the built-in parameter, one can find designs with power comparable to the fixed equal randomisation’s but a greater number of patients responded in the trials.

Sparse deep predictive coding captures contour integration capabilities of the early visual system.

Both neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-dependent information in the early visual cortex. While numerous models have accounted for feedback effects at either neural or representational level, none of them were able to bind those two levels of analysis. Is it possible to describe feedback effects at both levels using the same model? We answer this question by combining Predictive Coding (PC) and Sparse Coding (SC) into a hierarchical and convolutional framework applied to realistic problems. In the Sparse Deep Predictive Coding (SDPC) model, the SC component models the internal recurrent processing within each layer, and the PC component describes the interactions between layers using feedforward and feedback connections. Here, we train a 2-layered SDPC on two different databases of images, and we interpret it as a model of the early visual system (V1 & V2). We first demonstrate that once the training has converged, SDPC exhibits oriented and localized receptive fields in V1 and more complex features in V2. Second, we analyze the effects of feedback on the neural organization beyond the classical receptive field of V1 neurons using interaction maps. These maps are similar to association fields and reflect the Gestalt principle of good continuation. We demonstrate that feedback signals reorganize interaction maps and modulate neural activity to promote contour integration. Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images. Therefore, the SDPC captures the association field principle at the neural level which results in a better reconstruction of blurred images at the representational level.