Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Prestimulus default mode activity influences depth of processing and recognition in an emotional memory task

Low self-referential thoughts are associated with better concentration, which leads to deeper encoding and increases learning and subsequent retrieval. There is evidence that being engaged in externally rather than internally focused tasks is related to low neural activity in the default mode network (DMN) promoting open mind and the deep elaboration of new information. Thus, reduced DMN activity should lead to enhanced concentration, comprehensive stimulus evaluation including emotional categorization, deeper stimulus processing, and better long-term retention over one whole week. In this fMRI study, we investigated brain activation preceding and during incidental encoding of emotional pictures and on subsequent recognition performance. During fMRI, 24 subjects were exposed to 80 pictures of different emotional valence and subsequently asked to complete an online recognition task one week later. Results indicate that neural activity within the medial temporal lobes during encoding predicts subsequent memory performance. Moreover, a low activity of the default mode network preceding incidental encoding leads to slightly better recognition performance independent of the emotional perception of a picture. The findings indicate that the suppression of internally-oriented thoughts leads to a more comprehensive and thorough evaluation of a stimulus and its emotional valence. Reduced activation of the DMN prior to stimulus onset is associated with deeper encoding and enhanced consolidation and retrieval performance even one week later. Even small prestimulus lapses of attention influence consolidation and subsequent recognition performance. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.

RNAs and ribonucleoproteins in recognition and catalysis.

CONTENTS. 1. Did life begin with catalytic RNA?–2. Self-splicing and self-cleaving RNAs–2.1 Self-splicing of group I introns – 2.2 Self-splicing of group II introns – 2.3 Self-cleaving RNAs–3. Splicing mediated by trans-acting factors–3.1 Group III introns – 3.2 Splicing of nuclear pre-mRNAs – 3.3 Trans-splicing – 3.4 Is nuclear pre-mRNA splicing evolutionarily related to group I and group II self-splicing?– 3.5 Non-RNA mediated splicing of tRNAs–4. Processing of ribosomal precursor RNAs–5. Processing of pre-mRNA 3′ ends–5.1 Polyadenylation – 5.2 Histone pre-mRNA 3′ processing–6. Other RNPs involved in metabolic mechanisms–6.1 5′ end processing of pre-tRNAs by RNase P – 6.2 The signal recognition particle – 6.3 Telomerase – 6.4 RNA editing in trypanosomatid mitochondria–7. Why RNA?

Predictors for DSM-5 nonsuicidal self-injury in female adolescent inpatients: The role of childhood maltreatment, alexithymia, and dissociation

The purpose of this study was to examine the relationship between various adverse childhood experiences, alexithymia, and dissociation in predicting nonsuicidal self-injury (NSSI) in an inpatient sample of female adolescents. Seventy-two adolescents (aged 14–18 years) with NSSI disorder (n=46) or mental disorders without NSSI (n=26) completed diagnostic interviews and self-report measures to assess NSSI disorder according to the DSM-5 criteria, childhood maltreatment, alexithymia, and dissociation. Alexithymia and dissociation were highly prevalent in both study groups. Multivariate logistic regression analyses indicated that only alexithymia was a significant predictor for NSSI, whereas childhood maltreatment and dissociation had no predictive influence. The association between alexithymia and NSSI emphasizes the significance of emotion regulation training for female adolescents with NSSI. Efforts to reduce NSSI behavior should therefore foster skills to heighten the perception and recognition of one’s own emotions.

Using modular neural networks to model self-consciousness and self-representation for artificial entities

Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. Self-consciousness is only possible if an individual is intelligent enough to formulate an abstract self-representation. Moreover, it necessarily entails the capability of referencing and using this elf-representation in connection with other cognitive features, such as inference, and the anticipation of the consequences of both one’s own and other individuals’ acts. In this paper, a cognitive architecture for self-consciousness is proposed. This cognitive architecture includes several modules: abstraction, self-representation, other individuals'representation, decision and action modules. It includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN). For model testing, a virtual environment has been implemented. This virtual environment can be described as a holonic system or holarchy, meaning that it is composed of autonomous entities that behave both as a whole and as part of a greater whole. The system is composed of a certain number of holons interacting. These holons are equipped with cognitive features, such as sensory perception, and a simplified model of personality and self-representation. We explain holons’ cognitive architecture that enables dynamic self-representation. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.

Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.

Prion crystalization model and its application to recognition pattern

This paper introduces APA (?Artificial Prion Assembly?): a pattern recognition system based on artificial prion crystalization. Specifically, the system exhibits the capability to classify patterns according to the resulting prion self- assembly simulated with cellular automata. Our approach is inspired in the biological process of proteins aggregation, known as prions, which are assembled as amyloid fibers related with neurodegenerative disorders.

Striking sequence similarity in inter- and intra-specific comparisons of class I SLG alleles from Brassica oleracea and Brassica campestris: Implications for the evolution and recognition mechanism

Self-incompatibility in Brassica is controlled by a single multi-allelic locus (S locus), which contains at least two highly polymorphic genes expressed in the stigma: an S glycoprotein gene (SLG) and an S receptor kinase gene (SRK). The putative ligand-binding domain of SRK exhibits high homology to the secretory protein SLG, and it is believed that SLG and SRK form an active receptor kinase complex with a self-pollen ligand, which leads to the rejection of self-pollen. Here, we report 31 novel SLG sequences of Brassica oleracea and Brassica campestris. Sequence comparisons of a large number of SLG alleles and SLG-related genes revealed the following points. (i) The striking sequence similarity observed in an inter-specific comparison (95.6% identity between SLG14 of B. oleracea and SLG25 of B. campestris in deduced amino acid sequence) suggests that SLG diversification predates speciation. (ii) A perfect match of the sequences in hypervariable regions, which are thought to determine S specificity in an intra-specific comparison (SLG8 and SLG46 of B. campestris) and the observation that the hypervariable regions of SLG and SRK of the same S haplotype were not necessarily highly similar suggests that SLG and SRK bind different sites of the pollen ligand and that they together determine S specificity. (iii) Comparison of the hypervariable regions of SLG alleles suggests that intragenic recombination, together with point mutations, has contributed to the generation of the high level of sequence variation in SLG alleles. Models for the evolution of SLG/SRK are presented.

Molecular basis for the recognition of two structurally different major histocompatibility complex/peptide complexes by a single T-cell receptor

2C is a typical alloreactive cytotoxic T lymphocyte clone that recognizes two different ligands. These ligands are adducts of the allo-major histocompatibility complex (MHC) molecule H-2Ld and an endogenous octapeptide, and of the self-MHC molecule H-2Kb and another peptide. MHC-binding and T-cell assays with synthetic peptides in combination with molecular modeling studies were employed to analyze the structural basis for this crossreactivity. The molecular surfaces of the two complexes differ greatly in densities and distributions of positive and negative charges. However, modifications of the peptides that increase similarity decrease the capacities of the resulting MHC peptide complexes to induce T-cell responses. Moreover, the roles of the peptides in ligand recognition are different for self- and allo-MHC-restricted T-cell responses. The self-MHC-restricted T-cell responses were finely tuned to recognition of the peptide. The allo-MHC-restricted responses, on the other hand, largely ignore modifications of the peptide. The results strongly suggest that adaptation of the T-cell receptor to the different ligand structures, rather than molecular mimicry by the ligands, is the basis for the crossreactivity of 2C. This conclusion has important implications for T-cell immunology and for the understanding of immunological disorders.

Detection of competing DNA structures by thermal gradient gel electrophoresis: from self-association to triple helix formation by (G,A)-containing oligonucleotides

Sequence-specific recognition of DNA can be achieved by triple helix-forming oligonucleotides that bind to the major groove of double-helical DNA. These oligonucleotides have been used as sequence-specific DNA ligands for various purposes, including sequence-specific gene regulation in the so-called ‘antigene strategy’. In particular, (G,A)-containing oligonucleotides can form stable triple helices under physiological conditions. However, triplex formation may be in competition with self-association of these oligonucleotides. For biological applications it would be interesting to identify the conditions under which one structure is favoured as compared to the other(s). Here we have directly studied competition between formation of a parallel (G,A) homoduplex and that of a triple helix by a 13 nt (G,A)-containing oligonucleotide. Temperature gradient gel electrophoresis allows simultaneous detection of competition between the two structures, because of their different temperature dependencies and gel electrophoretic mobilities, and characterisation of this competition.

SRPDB (Signal Recognition Particle Database)

Signal recognition particle (SRP) is a stable cytoplasmic ribonucleoprotein complex that serves to translocate secretory proteins across membranes during translation. The SRP Database (SRPDB) provides compilations of SRP components, ordered alphabetically and phylogenetically. Alignments emphasize phylogenetically-supported base pairs in SRP RNA and conserved residues in the proteins. Data are provided in various formats including a column arrangement for improved access and simplified computational usability. Included are motifs for identification of new sequences, SRP RNA secondary structure diagrams, 3-D models and links to high-resolution structures. This release includes 11 new SRP RNA sequences (total of 129), two protein SRP9 sequences (total of seven), two protein SRP14 sequences (total of 10), two protein SRP19 sequences (total of 16), 10 new SRP54 (ffh) sequences (total of 66), two protein SRP68 sequences (total of seven) and two protein SRP72 sequences (total of nine). Seven sequences of the SRP receptor α-subunit and its FtsY homolog (total of 51) are new. Also considered are β-subunit of SRP receptor, Flhf, Hbsu, CaM kinase II and cpSRP43. Access to SRPDB is at http://psyche.uthct.edu/dbs/SRPDB/SRPDB.html and the European mirror http://www.medkem.gu.se/dbs/SRPDB/SRPDB.html

Synaptic pattern formation during cellular recognition

Cell–cell recognition often requires the formation of a highly organized pattern of receptor proteins (a synapse) in the intercellular junction. Recent experiments [e.g., Monks, C. R. F., Freiberg, B. A., Kupfer, H., Sciaky, N. & Kupfer, A. (1998) Nature (London) 395, 82–86; Grakoui, A., Bromley, S. K., Sumen, C., Davis, M. M., Shaw, A. S., Allen, P. M. & Dustin, M. L. (1999) Science 285, 221–227; and Davis, D. M., Chiu, I., Fassett, M., Cohen, G. B., Mandelboim, O. & Strominger, J. L. (1999) Proc. Natl. Acad. Sci. USA 96, 15062–15067] vividly demonstrate a complex evolution of cell shape and spatial receptor–ligand patterns (several microns in size) in the intercellular junction during immunological synapse formation. The current view is that this dynamic rearrangement of proteins into organized supramolecular activation clusters is driven primarily by active cytoskeletal processes [e.g., Dustin, M. L. & Cooper, J. A. (2000) Nat. Immunol. 1, 23–29; and Wulfing, C. & Davis, M. M. (1998) Science 282, 2266–2269]. Here, aided by a quantitative analysis of the relevant physico-chemical processes, we demonstrate that the essential characteristics of synaptic patterns observed in living cells can result from spontaneous self-assembly processes. Active cellular interventions are superimposed on these self-organizing tendencies and may also serve to regulate the spontaneous processes. We find that the protein binding/dissociation characteristics, protein mobilities, and membrane constraints measured in the cellular environment are delicately balanced such that the length and time scales of spontaneously evolving patterns are in near-quantitative agreement with observations for synapse formation between T cells and supported membranes [Grakoui, A., Bromley, S. K., Sumen, C., Davis, M. M., Shaw, A. S., Allen, P. M. & Dustin, M. L. (1999) Science 285, 221–227]. The model we present provides a common way of analyzing immunological synapse formation in disparate systems (e.g., T cell/antigen-presenting cell junctions with different MHC-peptides, natural killer cells, etc.).

Structural Analysis and Molecular Model of a Self-Incompatibility RNase from Wild Tomato1

Self-incompatibility RNases (S-RNases) are an allelic series of style glycoproteins associated with rejection of self-pollen in solanaceous plants. The nucleotide sequences of S-RNase alleles from several genera have been determined, but the structure of the gene products has only been described for those from Nicotiana alata. We report on the N-glycan structures and the disulfide bonding of the S3-RNase from wild tomato (Lycopersicon peruvianum) and use this and other information to construct a model of this molecule. The S3-RNase has a single N-glycosylation site (Asn-28) to which one of three N-glycans is attached. S3-RNase has seven Cys residues; six are involved in disulfide linkages (Cys-16-Cys-21, Cys-46-Cys-91, and Cys-166-Cys-177), and one has a free thiol group (Cys-150). The disulfide-bonding pattern is consistent with that observed in RNase Rh, a related RNase for which radiographic-crystallographic information is available. A molecular model of the S3-RNase shows that four of the most variable regions of the S-RNases are clustered on one surface of the molecule. This is discussed in the context of recent experiments that set out to determine the regions of the S-RNase important for recognition during the self-incompatibility response.

Expression of a second receptor rescues self-specific T cells from thymic deletion and allows activation of autoreactive effector function.

Allelic exclusion at the T-cell receptor alpha chain locus is incomplete resulting in the generation of T cells that express two T-cell receptors. The potential involvement of such T cells in autoimmunity has been suggested [Padovan, E., Casorati, G., Dellabona, P., Meyer, S., Brockhaus, M. & Lanzavecchia, A. (1993) Science 262, 422-424; Heath, W. R. & Miller, J. F. A. P. (1993) J. Exp. Med. 178, 1807-1811]. Here we show that expression of a second T-cell receptor can rescue T cells with autospecific receptors from thymic deletion and allow their exit into the periphery. Dual receptor T cells, created by constitutive expression of two transgenic T-cell receptors on a Rag1-/- background, are tolerant to self by maintaining low levels of autospecific receptor, but selfreactive effector function (killing) can be induced through activation via the second receptor. This opens the possibility that T cells carrying two receptors in the periphery of normal individuals contain putatively autoreactive cells that could engage in autoimmune effector functions after recognition of an unrelated environmental antigen.

Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.