Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

Automated counting of bacterial colony forming units on agar plates.

Manual counting of bacterial colony forming units (CFUs) on agar plates is laborious and error-prone. We therefore implemented a colony counting system with a novel segmentation algorithm to discriminate bacterial colonies from blood and other agar plates.A colony counter hardware was designed and a novel segmentation algorithm was written in MATLAB. In brief, pre-processing with Top-Hat-filtering to obtain a uniform background was followed by the segmentation step, during which the colony images were extracted from the blood agar and individual colonies were separated. A Bayes classifier was then applied to count the final number of bacterial colonies as some of the colonies could still be concatenated to form larger groups. To assess accuracy and performance of the colony counter, we tested automated colony counting of different agar plates with known CFU numbers of S. pneumoniae, P. aeruginosa and M. catarrhalis and showed excellent performance.

Modular training in vascular and endovascular surgery exemplified by the Vascular International Foundation and School

The partial shift from patient to model is a reasonable and necessary paradigm shift in surgery in order to increase patient safety and to adapt to the reduced training time periods in hospitals and increased quality demands. Since 1991 the Vascular International Foundation and School has carried out many training courses with more than 2,500 participants. The modular build training system allows to teach many open vascular and endovascular surgical techniques on lifelike models with a pulsatile circulation. The simulation courses cannot replace training in operating rooms but are suitable for supporting the cognitive and associative stages for achieving motor skills. Scientific evaluation of the courses has continually shown that the training principle established since 1991 can lead to significant learning success. They are extremely useful not only for beginners but also for experienced vascular surgeons. They can help to shorten the learning curve, to learn new techniques or to refine previously used techniques in all stages of professional development. Keywords Advanced training · Advanced training regulations · Training model · Vascular International · Certification

An epidemiological evaluation of pediatric long bone fractures - a retrospective cohort study of 2716 patients from two Swiss tertiary pediatric hospitals.

BACKGROUND Children and adolescents are at high risk of sustaining fractures during growth. Therefore, epidemiological assessment is crucial for fracture prevention. The AO Comprehensive Injury Automatic Classifier (AO COIAC) was used to evaluate epidemiological data of pediatric long bone fractures in a large cohort. METHODS Data from children and adolescents with long bone fractures sustained between 2009 and 2011, treated at either of two tertiary pediatric surgery hospitals in Switzerland, were retrospectively collected. Fractures were classified according to the AO Pediatric Comprehensive Classification of Long Bone Fractures (PCCF). RESULTS For a total of 2716 patients (60% boys), 2807 accidents with 2840 long bone fractures (59% radius/ulna; 21% humerus; 15% tibia/fibula; 5% femur) were documented. Children's mean age (SD) was 8.2 (4.0) years (6% infants; 26% preschool children; 40% school children; 28% adolescents). Adolescent boys sustained more fractures than girls (p < 0.001). The leading cause of fractures was falls (27%), followed by accidents occurring during leisure activities (25%), at home (14%), on playgrounds (11%), and traffic (11%) and school accidents (8%). There was boy predominance for all accident types except for playground and at home accidents. The distribution of accident types differed according to age classes (p < 0.001). Twenty-six percent of patients were classed as overweight or obese - higher than data published by the WHO for the corresponding ages - with a higher proportion of overweight and obese boys than in the Swiss population (p < 0.0001). CONCLUSION Overall, differences in the fracture distribution were sex and age related. Overweight and obese patients seemed to be at increased risk of sustaining fractures. Our data give valuable input into future development of prevention strategies. The AO PCCF proved to be useful in epidemiological reporting and analysis of pediatric long bone fractures.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Noninvasive Urinary Metabolomic Profiling Identifies Diagnostic and Prognostic Markers in Lung Cancer

Lung cancer remains the most common cause of cancer deaths worldwide, yet there is currently a lack of diagnostic noninvasive biomarkers that could guide treatment decisions. Small molecules (<1,500 Da) were measured in urine collected from 469 patients with lung cancer and 536 population controls using unbiased liquid chromatography/mass spectrometry. Clinical putative diagnostic and prognostic biomarkers were validated by quantitation and normalized to creatinine levels at two different time points and further confirmed in an independent sample set, which comprises 80 cases and 78 population controls, with similar demographic and clinical characteristics when compared with the training set. Creatine riboside (IUPAC name: 2-{2-[(2R,3R,4S,5R)-3,4-dihydroxy-5-(hydroxymethyl)-oxolan-2-yl]-1-methylcarbamimidamido}acetic acid), a novel molecule identified in this study, and N-acetylneuraminic acid (NANA) were each significantly (P < 0.00001) elevated in non-small cell lung cancer and associated with worse prognosis [HR = 1.81 (P = 0.0002), and 1.54 (P = 0.025), respectively]. Creatine riboside was the strongest classifier of lung cancer status in all and stage I-II cases, important for early detection, and also associated with worse prognosis in stage I-II lung cancer (HR = 1.71, P = 0.048). All measurements were highly reproducible with intraclass correlation coefficients ranging from 0.82 to 0.99. Both metabolites were significantly (P < 0.03) enriched in tumor tissue compared with adjacent nontumor tissue (N = 48), thus revealing their direct association with tumor metabolism. Creatine riboside and NANA may be robust urinary clinical metabolomic markers that are elevated in tumor tissue and associated with early lung cancer diagnosis and worse prognosis.

Universal ontology: attentive tracking of objects and substances across languages and over development

Previous research has demonstrated that adults are successful at visually tracking rigidly moving items, but experience great difficulties when tracking substance-like ‘‘pouring’’ items. Using a comparative approach, we investigated whether the presence/absence of the grammatical count–mass distinction influences adults and children’s ability to attentively track objects versus substances. More specifically, we aimed to explore whether the higher success at tracking rigid over substance-like items appears universally or whether speakers of classifier languages (like Japanese, not marking the object–substance distinction) are advantaged at tracking substances as compared to speakers of non-classifier languages (like Swiss German, marking the object–substance distinction). Our results supported the idea that language has no effect on low-level cognitive processes such as the attentive visual processing of objects and substances. We concluded arguing that the tendency to prioritize objects is universal and independent of specific characteristics of the language spoken.

Developing synaesthesia: A primer (Editorial)

Synaesthesia is a variation of human experience that involves the automatic activation of unusual concurrent experiences in response to ordinary inducing stimuli. The causes for the development of synaesthesia are not well understood yet. Synaesthesia may have a genetic basis resulting in enhanced cortical connectivity during development. However, in some cases synaesthesia has a sudden onset, for example, caused by posthypnotic suggestions, drug exposure, or brain injury. Moreover, associative learning during a critical developmental period also seems to play an important role. Synaesthesia may even be acquired by training in adulthood. In this research topic, we bring together topical hypotheses, theories and empirical studies about the development of synaesthesia.

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.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Cathodal HD-tDCS on the right V5 improves motion perception in humans

Brain lesions in the visual associative cortex are known to impair visual perception, i.e., the capacity to correctly perceive different aspects of the visual world, such as motion, color, or shapes. Visual perception can be influenced by non-invasive brain stimulation such as transcranial direct current stimulation (tDCS). In a recently developed technique called high definition (HD) tDCS, small HD-electrodes are used instead of the sponge electrodes in the conventional approach. This is believed to achieve high focality and precision over the target area. In this paper we tested the effects of cathodal and anodal HD-tDCS over the right V5 on motion and shape perception in a single blind, within-subject, sham controlled, cross-over trial. The purpose of the study was to prove the high focality of the stimulation only over the target area. Twenty one healthy volunteers received 20 min of 2 mA cathodal, anodal and sham stimulation over the right V5 and their performance on a visual test was recorded. The results showed significant improvement in motion perception in the left hemifield after cathodal HD-tDCS, but not in shape perception. Sham and anodal HD-tDCS did not affect performance. The specific effect of influencing performance of visual tasks by modulating the excitability of the neurons in the visual cortex might be explained by the complexity of perceptual information needed for the tasks. This provokes a "noisy" activation state of the encoding neuronal patterns. We speculate that in this case cathodal HD-tDCS may focus the correct perception by decreasing global excitation and thus diminishing the "noise" below threshold.

Herbivore-Induced Plant Volatiles Can Serve as Host Location Cues for a Generalist and a Specialist Egg Parasitoid

Herbivore-induced plant volatiles are important host finding cues for larval parasitoids, and similarly, insect oviposition might elicit the release of plant volatiles functioning as host finding cues for egg parasitoids. We hypothesized that egg parasitoids also might utilize HIPVs of emerging larvae to locate plants with host eggs. We, therefore, assessed the olfactory response of two egg parasitoids, a generalist, Trichogramma pretiosum (Tricogrammatidae), and a specialist, Telenomus remus (Scelionidae) to HIPVs. We used a Y-tube olfactometer to tests the wasps’ responses to volatiles released by young maize plants that were treated with regurgitant from caterpillars of the moth Spodoptera frugiperda (Noctuidae) or were directly attacked by the caterpillars. The results show that the generalist egg parasitoid Tr. pretiosum is innately attracted by volatiles from freshly-damaged plants 0–1 and 2–3 h after regurgitant treatment. During this interval, the volatile blend consisted of green leaf volatiles (GLVs) and a blend of aromatic compounds, mono- and homoterpenes, respectively. Behavioral assays with synthetic GLVs confirmed their attractiveness to Tr. pretiosum. The generalist learned the more complex volatile blends released 6–7 h after induction, which consisted mainly of sesquiterpenes. The specialist T. remus on the other hand was attracted only to volatiles emitted from fresh and old damage after associating these volatiles with oviposition. Taken together, these results strengthen the emerging pattern that egg and larval parasitoids behave in a similar way in that generalists can respond innately to HIPVs, while specialists seems to rely more on associative learning.

Clitics, affixes, and wordhood in Garifuna (Arawak): A multidimensional continuum of morphosyntactic boundness

Recent research on wordhood and morphosyntactic boundness suggests that the domains word and clitic do not lend themselves to cross-linguistic categorization but must be defined language specifically. In most languages, it is necessary to define word on two separate levels, the phonological word (p-word) and the grammatical word (g-word), and to describe mismatches between the two. This paper defines those domains for Garifuna, an Arawak language spoken in Honduras, Central America. Garifuna has auxiliary and classifier constructions which make up two p-words, and only one g-word. P-words made up of more than one g-word involve second position enclitics, word scope clitics, and proclitic connectives and prepositions. Garifuna clitics are typically unstressed, able to attach to hosts of any word class and able to string together into clusters. Enclitics are used to express tense-aspect, modality, and adverbial meanings, among others. In other languages, clitic clusters tend to display a fixed order; Garifuna clitic order seems quite free, although certain orders are preferred. Also, contrary to cross-linguistic tendencies, proclitic connectives can act as hosts for enclitic clusters, contradicting the commonly used definition of clitics as phonologically weak elements that need to attach to a host to form a p-word; such clitic-only p-words are problematic for traditional definitions of clitics.

The Role of Fresh versus Old Leaf Damage in the Attraction of Parasitic Wasps to Herbivore-Induced Maize Volatiles

The odor produced by a plant under herbivore attack is often used by parasitic wasps to locate hosts. Any type of surface damage commonly causes plant leaves to release so-called green leaf volatiles, whereas blends of inducible compounds are more specific for herbivore attack and can vary considerably among plant genotypes. We compared the responses of naïve and experienced parasitoids of the species Cotesia marginiventris and Microplitis rufiventris to volatiles from maize leaves with fresh damage (mainly green leaf volatiles) vs. old damage (mainly terpenoids) in a six-arm olfactometer. These braconid wasps are both solitary endoparasitoids of lepidopteran larvae, but differ in geographical origin and host range. In choice experiments with odor blends from maize plants with fresh damage vs. blends from plants with old damage, inexperienced C. marginiventris showed a preference for the volatiles from freshly damaged leaves. No such preference was observed for inexperienced M. rufiventris. After an oviposition experience in hosts feeding on maize plants, C. marginiventris females were more attracted by a mixture of volatiles from fresh and old damage. Apparently, C. marginiventris has an innate preference for the odor of freshly damaged leaves, and this preference shifts in favor of a blend containing a mixture of green leaf volatiles plus terpenoids, after experiencing the latter blend in association with hosts. M. rufiventris responded poorly after experience and preferred fresh damage odors. Possibly, after associative learning, this species uses cues that are more directly related with the host presence, such as volatiles from host feces, which were not present in the odor sources offered in the olfactometer. The results demonstrate the complexity of the use of plant volatiles by parasitoids and show that different parasitoid species have evolved different strategies to exploit these signals.

Molecular profiling of lung adenosquamous carcinoma: hybrid or genuine type?

Lung adenosquamous carcinoma is a particular subtype of non-small cell lung carcinoma that is defined by the coexistence of adenocarcinoma and squamous cell carcinoma components. The aim of this study was to assess the mutational profile in each component of 16 adenosquamous carcinoma samples from a Caucasian population by a combination of next generation sequencing using the cancer hotspot panel as well as the colon and lung cancer panel and FISH. Identified mutations were confirmed by Sanger sequencing of DNA from cancer cells of each component collected by Laser Capture microdissection. Mutations typical for adenocarcinoma as well as squamous cell carcinoma were identified. Driver mutations were predominantly in the trunk suggesting a monoclonal origin of adenosquamous carcinoma. Most remarkably, EGFR mutations and mutations in the PI3K signaling pathway, which accounted for 30% and 25% of tumors respectively, were more prevalent while KRAS mutations were less prevalent than expected for a Caucasian population. Surprisingly, expression of classifier miR-205 was intermediate between that of classical adenocarcinoma and squamous cell carcinoma suggesting that adenosquamous carcinoma is a transitional stage between these tumor types. The high prevalence of therapy-relevant targets opens new options of therapeutic intervention for adenosquamous carcinoma patients.