Social reader: towards browsing the social web

We describe Social Reader, a feed-reader-plus-social-network aggregator that mines comments from social media in order to display a user’s relational neighborhood as a navigable social network. Social Reader’s network visualization enhances mutual awareness of blogger communities, facilitates their exploration and growth with a fully dragn- drop interface, and provides novel ways to filter and summarize people, groups, blogs and comments. We discuss the architecture behind the reader, highlight tasks it adds to the workflow of a typical reader, and assess their cost. We also explore the potential of mood-based features in social media applications. Mood is particularly relevant to social media, reflecting the personal nature of the medium. We explore two prototype mood-based features: colour coding the mood of recent posts according to a valence/arousal map, and a mood-based abstract of recent activity using image media. A six week study of the software involving 20 users confirmed the usefulness of the novel visual display, via a quantitative analysis of use logs, and an exit survey.     

The Coolest Way to Generate Binary Strings

Pick a binary string of length n and remove its first bit b. Now insert b after the first remaining 10, or insert $\overline{b}$ at the end if there is no remaining 10. Do it again. And again. Keep going! Eventually, you will cycle through all 2 ⁿ of the binary strings of length n. For example, are the binary strings of length n=4, where and . And if you only want strings with weight (number of 1s) between ? and u? Just insert b instead of $\overline{b}$ when the result would have too many 1s or too few 1s. For example, are the strings with n=4, ?=0 and u=2. This generalizes ‘cool-lex’ order by Ruskey and Williams (The coolest way to generate combinations, Discrete Mathematics) and we present two applications of our ‘cooler’ order. First, we give a loopless algorithm for generating binary strings with any weight range in which successive strings have Levenshtein distance two. Second, we construct de Bruijn sequences for (i) ?=0 and any u (maximum specified weight), (ii) any ? and u=n (minimum specified weight), and (iii) odd u?? (even size weight range). For example, all binary strings with n=6, ?=1, and u=4 appear once (cyclically) in . We also investigate the recursive structure of our order and show that it shares certain sublist properties with lexicographic order.     

A customised approach to determining the geomorphic effectiveness of small flood events in a regulated river

The construction of dams significantly alters flow and sediment regimes with subsequent deleterious effects on the morphological and ecological character of rivers. Effective experimental floods can ameliorate the downstream geomorphic impacts of dams. The traditional view is that large floods are required to perform effective geomorphic work, and the geomorphic outcomes of small floods are often overlooked. Many river restoration frameworks do not consider small floods. Yet, there is evidence that the hydrological characteristics that ameliorate specific geomorphic impacts in a river are unique to each river, and a customised approach to setting the right mix of floods (including small experimental floods) is needed. In this study, we modify an existing flood effectiveness model developed for large floods, for determining the geomorphic effectiveness of small floods in a highly regulated Australian river. Two flood classes were added to the model (medium peak stream power and moderate total energy expenditure), and the flood power characteristics were rescaled to reflect the relative difference in the magnitude of the small floods and the magnitude of the geomorphic work performed. Using a step‐wise approach, this customised model determined the geomorphic effectiveness of small floods. The best flood for ameliorating the geomorphic impacts of flow regulation had medium to long duration (10 to 51 days), high peak unit stream power (77 to 123 Wm^?2) and moderate to large total energy expenditure (78,600 to 342,320 × 10³ J). This approach to determining flood effectiveness for small floods is applicable to other geomorphically impacted river channels downstream of dams and can be used to inform experimental flood releases for geomorphic outcomes.     

Unsupervised context detection using wireless signals

The sensing context plays an important role in many pervasive and mobile computing applications. Continuing from previous work [D. Phung, B. Adams, S. Venkatesh, Computable social patterns from sparse sensor data, in: Proceedings of First International Workshop on Location Web, World Wide Web Conference (WWW), New York, NY, USA, 2008, ACM 69–72.], we present an unsupervised framework for extracting user context in indoor environments with existing wireless infrastructures. Our novel approach casts context detection into an incremental, unsupervised clustering setting. Using WiFi observations consisting of access point identification and signal strengths freely available in office or public spaces, we adapt a density-based clustering technique to recover basic forms of user contexts that include user motion state and significant places the user visits from time to time. High-level user context, termed rhythms, comprising sequences of significant places are derived from the above low-level context by employing probabilistic clustering techniques, latent Dirichlet allocation and its n-gram temporal extension. These user contexts can enable a wide range of context-ware application services. Experimental results with real data in comparison with existing methods are presented to validate the proposed approach. Our motion classification algorithm operates in real-time, and achieves a 10% improvement over an existing method; significant locations are detected with over 90% accuracy and near perfect cluster purity. Richer indoor context and meaningful rhythms, such as typical daily routines or meeting patterns, are also inferred automatically from collected raw WiFi signals.     

Arginine 206 of the C5a receptor is critical for ligand recognition and receptor activation by C-terminal hexapeptide analogs

C5a is a 74-amino-acid glycoprotein whose receptor is a member of the rhodopsin superfamily. While antagonists have been generated to many of these receptors, similar efforts directed at family members whose natural ligands are proteins have met with little success. The recent development of hexapeptide analogs of C5a has allowed us to begin elucidation of the molecular events that lead to activation by combining a structure/activity study of the ligand with receptor mutagenesis. Removal of the hexapeptide's C-terminal arginine reduces affinity by 100-fold and eliminates the ability of the ligand to activate the receptor. Both the guanidino side chain and the free carboxyl of the arginine participate in the interaction. The guanidino group makes the energy-yielding contact with the receptor, while the free carboxylate negates "electrostatic" interference with Arg-206 of the receptor. It is the apparent movement Arg-206 induced by this set of interactions that is responsible for activation, since conversion of Arg-206 to alanine eliminates the agonist activity of the hexapeptides. Surprisingly, activation is a nearly energy-neutral event and may reflect the binding process rather than the final resting site of the ligand.     

The genome of Treponema pallidum: new light on the agent of syphilis

Covert brain activity related to task-free, spontaneous (i.e. unrequested), emotional evaluation of human face images was analysed in 27-channel averaged event-related potential (ERP) map series recorded from 18 healthy subjects while observing random sequences of face images without further instructions. After recording, subjects self-rated each face image on a scale from "liked" to "disliked". These ratings were used to dichotomize the face images into the affective evaluation categories of "liked" and "disliked" for each subject and the subjects into the affective attitudes of "philanthropists" and "misanthropists" (depending on their mean rating across images). Event-related map series were averaged for "liked" and "disliked" face images and for "philanthropists" and "misanthropists". The spatial configuration (landscape) of the electric field maps was assessed numerically by the electric gravity center, a conservative estimate of the mean location of all intracerebral, active, electric sources. Differences in electric gravity center location indicate activity of different neuronal populations. The electric gravity center locations of all event-related maps were averaged over the entire stimulus-on time (450 ms). The mean electric gravity center for disliked faces was located (significant across subjects) more to the right and somewhat more posterior than for liked faces. Similar differences were found between the mean electric gravity centers of misanthropists (more right and posterior) and philanthropists. Our neurophysiological findings are in line with neuropsychological findings, revealing visual emotional processing to depend on affective evaluation category and affective attitude, and extending the conclusions to a paradigm without directed task.     

Electrophysiological evidence for a postperceptual locus of suppression during the attentional blink

When an observer detects a target in a rapid stream of visual stimuli, there is a brief period of time during which the detection of subsequent targets is impaired. In this study, event-related potentials (ERPs) were recorded from normal adult observers to determine whether this "attentional blink" reflects a suppression of perceptual processes or an impairment in postperceptual processes. No suppression was observed during the attentional blink interval for ERP components corresponding to sensory processing (the P1 and N1 components) or semantic analysis (the N400 component). However, complete suppression was observed for an ERP component that has been hypothesized to reflect the updating of working memory (the P3 component). Results indicate that the attentional blink reflects an impairment in a postperceptual stage of processing.     

Paramagnetic 1H-NMR relaxation probes of stereoselectivity in metalloporphyrin catalyzed olefin epoxidation

Enantioselective catalytic epoxidation of olefins is an important problem from both practical and mechanistic points of view. The origins of chiral induction by asymmetric porphyrin and salen complexes were investigated by FT-NMR T1 relaxation techniques. A new chiral vaulted porphyrin (1) that carries (S)-binaphthyl-L-alanine straps across both faces of the porphyrin macrocycle was synthesized and characterized. (R)-styrene oxide was obtained in > 90% ee in the initial stages of styrene epoxidation with F5PhIO catalyzed by 1-Fe(III)Cl. The transition state for olefin epoxidation with high-valent metal-oxo species was modeled by coordinating epoxides to paramagnetic copper complexes of the corresponding ligands. The epoxide enantiomer that better fit the chiral cavity of the catalyst, as revealed by T1 relaxation measurements, was also the major product of catalytic olefin epoxidation. These results are consistent with the "lock-and-key" mechanism of asymmetric catalysis by metalloporphyrins. The copper complex of a chiral salen ligand showed no differentiation in terms of T1 relaxation rates between the enantiomers of cis-beta-methylstyrene oxide in contrast to the high enantioselectivity observed for catalytic epoxidation.     

Periodic orbits: a new language for neuronal dynamics

A new nonlinear dynamical analysis is applied to complex behavior from neuronal systems. The conceptual foundation of this analysis is the abstraction of observed neuronal activities into a dynamical landscape characterized by a hierarchy of "unstable periodic orbits" (UPOs). UPOs are rigorously identified in data sets representative of three different levels of organization in mammalian brain. An analysis based on UPOs affords a novel alternative method of decoding, predicting, and controlling these neuronal systems.     

Model selection for covariance structures analysis in nursing research

Covariance structures analysis is often used in nursing research to appraise statistical models reflecting complex human health processes. The model selection approach in covariance structures analysis is designed to select the "best" model from a specified set of theoretically defensible, competing alternatives, all of which are viewed as approximations. Model selection criteria explicitly incorporate both model misfit in the population and sampling error to evaluate the set of models. The result is that interpretability of model parameters and goodness-of-fit are enhanced simultaneously. Relative merits of the model selection approach are identified in light of technical concerns, parsimony, and use of scientific theory in nursing.