The syndromology of anorectal malformations revisited: from patterns of associated malformations to the recognition of syndromes

ackground: Although the frequency of associated malformation is high, the incidence of inheritable syndromes is widely underestimated in children with anorectal malformation (ARM). Data sources: OMIM database, patient records and charts of the Department of Pediatric Surgery, Johannes Gutenberg-University, Mainz, Germany. Methods: We analyzed all associations, sequences and syndromes listed in the OMIM database that can be accompanied by ARM. A large cohort of children born with ARM was then retrospectively investigated as to the type of ARM, presence of additional malformations and possible categorization as a syndrome, sequence or association. For this process a syndrome finder was developed and employed. This simplistic tool allows for a rapid first check of possible syndromes before a more complex analysis is started using the OMIM database and consulting specialists. Results: Among 317 children with ARM, associated malformations were present in 77.7% of 127 children with high ARM, in 68.7% of 32 with intermediate ARM, and in 25.3% of 158 with a low type ARM. Three or more organ systems were involved in 29.1% children with high type ARM and 25% with intermediate ARM and 8.2% with a low type ARM. An association of the vertebral anal tracheo-esophageal renal (VATER) and vertebral anal cardiac tracheo-esophageal renal limb (VACTERL) type was found in a total of 35 patients. Before analysis, 11 syndromes and 35 associations which were not clear previously in this patient cohort were described. In other 17 patients, 14 syndromes and 3 associations were identified. Conclusions: The high number of only retrospectively identified syndromes suggests that a routine search is necessary in every patient with ARM and additional malformations.

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.

Emotion recognition: The role of featural and configural face information

Several studies investigated the role of featural and configural information when processing facial identity. A lot less is known about their contribution to emotion recognition. In this study, we addressed this issue by inducing either a featural or a configural processing strategy (Experiment 1) and by investigating the attentional strategies in response to emotional expressions (Experiment 2). In Experiment 1, participants identified emotional expressions in faces that were presented in three different versions (intact, blurred, and scrambled) and in two orientations (upright and inverted). Blurred faces contain mainly configural information, and scrambled faces contain mainly featural information. Inversion is known to selectively hinder configural processing. Analyses of the discriminability measure (A′) and response times (RTs) revealed that configural processing plays a more prominent role in expression recognition than featural processing, but their relative contribution varies depending on the emotion. In Experiment 2, we qualified these differences between emotions by investigating the relative importance of specific features by means of eye movements. Participants had to match intact expressions with the emotional cues that preceded the stimulus. The analysis of eye movements confirmed that the recognition of different emotions rely on different types of information. While the mouth is important for the detection of happiness and fear, the eyes are more relevant for anger, fear, and sadness.

Systemic Determinants of the Information Systems Outsourcing Decision: A Comparative Study of German and United States Firms

The practice of information systems (IS) outsourcing is widely established among organizations. Nonetheless, evidence suggests that organizations differ considerably in the extent to which they deploy IS outsourcing. This variation has motivated research into the determinants of the IS outsourcing decision. Most of this research is based on the assumption that a decision on the outsourcing of a particular IS function is made independently of other IS functions. This modular view ignores the systemic nature of the IS function, which posits that IS effectiveness depends on how the various IS functions work together effectively. This study proposes that systemic influences are important criteria in evaluating the outsourcing option. It further proposes that the recognition of systemic influences in outsourcing decisions is culturally sensitive. Specifically, we provide evidence that systemic effects are factored into the IS outsourcing decision differently in more individualist cultures than in collectivist ones. Our results of a survey of United States and German firms indicate that perceived in-house advantages in the systemic impact of an IS function are, indeed, a significant determinant of IS outsourcing in a moderately individualist country (i.e., Germany), whereas insignificant in a strongly individualist country (i.e., the United States). The country differences are even stronger with regard to perceived in-house advantages in the systemic view of IS professionals. In fact, the direction of this impact is reversed in the United States sample. Other IS outsourcing determinants that were included as controls, such as cost efficiency, did not show significant country differences.

Sparse representation based action and gesture recognition

In this paper we present a solution to the problem of action and gesture recognition using sparse representations. The dictionary is modelled as a simple concatenation of features computed for each action or gesture class from the training data, and test data is classified by finding sparse representation of the test video features over this dictionary. Our method does not impose any explicit training procedure on the dictionary. We experiment our model with two kinds of features, by projecting (i) Gait Energy Images (GEIs) and (ii) Motion-descriptors, to a lower dimension using Random projection. Experiments have shown 100% recognition rate on standard datasets and are compared to the results obtained with widely used SVM classifier.

Managing the risks of medical technology: Do hazard surveillance systems provide an early warning of medical device problems?

The Food and Drug Administration (FDA) is responsible for risk assessment and risk management in the post-market surveillance of the U.S. medical device industry. One of the FDA regulatory mechanisms, the Medical Device Reporting System (MDR) is an adverse event reporting system intended to provide the FDA with advance warning of device problems. It includes voluntary reporting for individuals, and mandatory reporting for device manufacturers. ^ In a study of alleged breast implant safety problems, this research examines the organizational processes by which the FDA gathers data on adverse events and uses adverse event reporting systems to assess and manage risk. The research reviews the literature on problem recognition, risk perception, and organizational learning to understand the influence highly publicized events may have on adverse event reporting. Understanding the influence of an environmental factor, such as publicity, on adverse event reporting can provide insight into the question of whether the FDA's adverse event reporting system operates as an early warning system for medical device problems. ^ The research focuses on two main questions. The first question addresses the relationship between publicity and the voluntary and mandatory reporting of adverse events. The second question examines whether government agencies make use of these adverse event reports. ^ Using quantitative and qualitative methods, a longitudinal study was conducted of the number and content of adverse event reports regarding breast implants filed with the FDA's medical device reporting system during 1985–1991. To assess variation in publicity over time, the print media were analyzed to identify articles related to breast implant failures. ^ The exploratory findings suggest that an increase in media activity is related to an increase in voluntary reporting, especially following periods of intense media coverage of the FDA. However, a similar relationship was not found between media activity and manufacturers' mandatory adverse event reporting. A review of government committee and agency reports on the FDA published during 1976–1996 produced little evidence to suggest that publicity or MDR information contributed to problem recognition, agenda setting, or the formulation of policy recommendations. ^ The research findings suggest that the reporting of breast implant problems to FDA may reflect the perceptions and concerns of the reporting groups, a barometer of the volume and content of media attention. ^

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.

Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.

Implicit memory for the content but not the speaker of sleep-played messages

We presented 28 sentences uttered by 28 unfamiliar speakers to sleeping participants to investigate whether humans can encode new verbal messages, learn voices of unfamiliar speakers, and form associations between speakers and messages during EEG-defined deep sleep. After waking, participants performed three tests which assessed the unconscious recognition of sleep-played speakers, messages, and speaker-message associations. Recognition performance in all tests was at chance level. However, response latencies revealed implicit memory for sleep-played messages but neither for speakers nor for speaker-message combinations. Only participants with excellent implicit memory for sleep-played messages also displayed implicit memory for speakers but not speaker-message associations. Hence, deep sleep allows for the semantic encoding of novel verbal messages.

Cross -packing among retroviruses and characterization of the feline immunodeficiency virus (FIV) packaging signal: Implications for the development of FIV-based gene transfer systems

Feline immunodeficiency virus (FIV)-based gene transfer systems are being seriously considered for human gene therapy as an alternative to vectors based on primate lentiviruses, a genetically complex group of retroviruses capable of infecting non-dividing cells. The greater phylogenetic distance between the feline and primate lentiviruses is thought to reduce chances of the generation of recombinant viruses. However, safety of FIV-based vector systems has not been tested experimentally. Since primate lentiviruses such as human and simian immunodeficiency viruses (HIV/SIV) can cross-package each other's genomes, we tested this trait with respect to FIV. Unexpectedly, both feline and primate lentiviruses were reciprocally able to both cross-package and propagate each other's RNA genomes. This was largely due to the recognition of viral packaging signals by the heterologous proteins. However, a simple retrovirus such as Mason-Pfizer monkey virus (MPMV) was unable to package FIV RNA. Interestingly, FIV could package MPMV RNA, but not propagate it for further steps of replication. These findings suggest that upon co-infection of the same host, cross-packaging may allow distinct retroviruses to generate chimeric variants with unknown pathogenic potential. ^ In order to understand the packaging determinants in FIV, we conducted a detailed mutational analysis of the region thought to contain FIV packaging signal. We show that the first 90–120 nt of the 5′ untranslated region (UTR) and the first 90 nt of gag were simultaneously required for efficient FIV RNA packaging. These results suggest that the primary FIV packaging signal is multipartite and discontinuous, composed of two core elements separated by 150 nt of the 5 ′UTR. ^ The above studies are being used towards the development of safer FIV-based self-inactivating (SIN) vectors. These vectors are being designed to eliminate the ability of FIV transfer vector RNAs to be mobilized by primate lentiviral proteins that may be present in the target cells. Preliminary test of the first generation of these vectors has revealed that they are incapable of being propagated by feline proteins. The inability of FIV transfer vectors to express packageable vector RNA after integration should greatly increase the safety of FIV vectors for human gene therapy. ^