Atomistic simulations of 2D bicomponent self-assembly: from molecular recognition to self-healing

Supramolecular two-dimensional engineering epitomizes the design of complex molecular architectures through recognition events in multicomponent self-assembly. Despite being the subject of in-depth experimental studies, such articulated phenomena have not been yet elucidated in time and space with atomic precision. Here we use atomistic molecular dynamics to simulate the recognition of complementary hydrogen-bonding modules forming 2D porous networks on graphite. We describe the transition path from the melt to the crystalline hexagonal phase and show that self-assembly proceeds through a series of intermediate states featuring a plethora of polygonal types. Finally, we design a novel bicomponent system possessing kinetically improved self-healing ability in silico, thus demonstrating that a priori engineering of 2D self-assembly is possible.

Self-reactivity in the dimeric intravenous immunoglobulin fraction

Therapeutic intravenous immunoglobulin (IVIg) preparations contain antibodies reflecting the cumulative antigen experience of the donor population. IVIg contains variable amounts of monomeric and dimeric IgG, but there is little information available on their comparative antibody specificities. We have isolated highly purified fractions of monomeric and dimeric IgG by size-exclusion chromatography. Following treatment of all fractions at pH4, analyses by immunodot and immunocytology on human cell lines showed a preferential recognition of autoantigens in the dimeric IgG fraction. Investigation of the HEp-2 cytoplasmic proteome by 2D-PAGE, Western blot, and subsequent identification of IVIg reactive spots by mass spectrometry (LC-MS/MS) showed that IVIg recognized only a restricted set of the total proteins. Similar experiments showed that more antigens were recognized by the dimeric IgG fraction, especially when the dissociated dimer fraction was used, as compared to its monomeric counterpart. These observations are consistent with idiotype-anti-idiotype masking of auto-specific Abs in the dimeric fraction of IVIg.

Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Changes in emotion recognition relate to hypertension status

Background: Emotional processing in essential hypertension beyond self-report questionnaire has hardly been investigated. The aim of this study is to examine associations between hypertension status and recognition of facial affect. Methods: 25 healthy, non-smoking, medication-free men including 13 hypertensive subjects aged between 20 and 65 years completed a computer-based task in order to examine sensitivity of recognition of facial affect. Neutral faces gradually changed to a specific emotion in a pseudo-continuous manner. Slides of the six basic emotions (fear, sadness, disgust, happiness, anger, surprise) were chosen from the „NimStim Set“. Pictures of three female and three male faces were electronically morphed in 1% steps of intensity from 0% to 100% (36 sets of faces with 100 pictures each). Each picture of a set was presented for one second, ranging from 0% to 100% of intensity. Participants were instructed to press a stop button as soon as they recognized the expression of the face. After stopping a forced choice between the six basic emotions was required. As dependent variables, we recorded the emotion intensity at which the presentation was stopped and the number of errors (error rate). Recognition sensitivity was calculated as emotion intensity of correctly identified emotions. Results: Mean arterial pressure was associated with a significantly increased recognition sensitivity of facial affect for the emotion anger (ß = - .43, p = 0.03*, Δ R2= .110). There was no association with the emotions fear, sadness, disgust, happiness, and surprise (p’s > .0.41). Mean arterial pressure did not relate to the mean number of errors for any of the facial emotions. Conclusions: Our findings suggest that an increased blood pressure is associated with increased recognition sensitivity of facial affect for the emotion anger, if a face shows anger. Hypertensives perceive facial anger expression faster than normotensives, if anger is shown.

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.