Lung ultrasound is superior to lung auscultation and supine chest radiography for many respiratory conditions in human patients. Ultrasound diagnoses are based on easily learned patterns of sonographic findings and artifacts in standardized images. By applying the wet lung (ultrasound lung rockets or B-lines, representing interstitial edema) versus dry lung (A-lines with a glide sign) concept many respiratory conditions can be diagnosed or excluded. The ultrasound probe can be used as a visual stethoscope for the evaluation of human lungs because dry artifacts (A-lines with a glide sign) predominate over wet artifacts (ultrasound lung rockets or B-lines). However, the frequency and number of wet lung ultrasound artifacts in dogs with radiographically normal lungs is unknown. Thus, the primary objective was to determine the baseline frequency and number of ultrasound lung rockets in dogs without clinical signs of respiratory disease and with radiographically normal lung findings using an 8-view novel regionally based lung ultrasound examination called Vet BLUE. Frequency of ultrasound lung rockets were statistically compared based on signalment, body condition score, investigator, and reasons for radiography. Ten left-sided heart failure dogs were similarly enrolled. Overall frequency of ultrasound lung rockets was 11% (95% confidence interval, 6-19%) in dogs without respiratory disease versus 100% (95% confidence interval, 74-100%) in those with left-sided heart failure. The low frequency and number of ultrasound lung rockets observed in dogs without respiratory disease and with radiographically normal lungs suggests that Vet BLUE will be clinically useful for the identification of canine respiratory conditions.

The lack of B-lines in cats without respiratory disease (with radiographically normal lungs) and the predominance of B-lines in cats with left-sided CHF suggest that a regionally based LUS protocol may be clinically useful for the identification and evaluation of feline respiratory conditions.

Computer-mediated textual communication has become ubiquitous in recent years. Compared to face-to-face interactions, there is decreased bandwidth in affective information, yet studies show that interactions in this medium still produce rich and fulfilling affective outcomes. While overt communication (e.g., emoticons or explicit discussion of emotion) can explain some aspects of affect conveyed through textual dialogue, there may also be an underlying implicit affective channel through which participants perceive additional emotional information. To investigate this phenomenon, computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures. Analyses demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. Additionally, bodily expressions of posture and gesture correlated with student cognitive-affective states that were perceived by tutors through the implicit affective channel. Finally, posture and gesture complement each other in multimodal predictive models of student cognitive-affective states, explaining greater variance than either modality alone. This approach of empirically studying the implicit affective channel may identify details of human behavior that can inform the design of future textual dialogue systems modeled on naturalistic interaction.

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