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Most patients with advanced cancer develop skeletal muscle atrophy called sarcopenia, which reduces treatment tolerance and social activity and worsens the prognosis.

Objective: To evaluate changes in the levels of salivary irisin in chronic periodontitis, and to correlate the two. Methods: The analytical cross-sectional study was conducted at Fatima Memorial Hospital & College of Dentistry, Lahore, Pakistan, from September 2017 to March 2018, and comprised patients of either gender visiting the periodontic out-patient department. The subjects were divided into group I, which had periodontally healthy controls, and group II, which had an equal number of chronic periodontitis patients. Chronic periodontitis was assessed on basis of pocket probing depth, clinical attachment level, plaque percentage and bleeding on probing. Also, 4ml of un-stimulated saliva was collected for the quantification of irisin protein using enzyme-linked immunosorbent assay. Data was analysed using SPSS 25. Results: Of the 40 subjects, there were 20 (50%) in group I with 10 (50%) males and 10 (50%) females having an overall mean age of 37.60±2.58 years. The remaining 20 (50%) subjects were in group II with 16 (80%) males and 4 (20%) females having an overall mean age of 43.25±6.10 years. Mean salivary irisin level in group II was 6.80±3.97ng/ml compared to 3.99±2.48 ng/ml in group I (p=0.009). Periodontal clinical parameters in both the groups were positively but non-significantly correlated with salivary irisin levels (p>0.05) except for decreased plaque percentage in group I (p

Active learning aims to reduce the time and cost of developing speech recognition systems by selecting for transcription highly informative subsets from large pools of audio data. Previous evaluations at OpenKWS and IARPA BABEL have investigated data selection for low-resource languages in very constrained scenarios with 2-hour data selections given a 1-hour seed set. We expand on this to investigate what happens with larger selections and fewer constraints on language modeling data. Our results, on four languages from the final BABEL OP3 period, show that active learning is helpful at larger selections with consistent gains up to 14 hours. We also find that the impact of additional language model data is orthogonal to the impact of the active learning selection criteria.

We demonstrate the use of sequence pattern mining as applied to monitoring the usage of emailing software by clients with cognitive impairments. We show how Max Motif, a sequence-mining algorithm, can be applied using a stream-mining approach. Consequently, clinicians can now consider sequential patterns in their analysis of client emailing behaviors. Such analysis is part of the Think and Link project, which provides personalized email clients, a kind of assistive technology, to aid client communications that facilitate activities of daily living. By using the simplified, customized system, clients can now email, whereas they could not previously. By continuously monitoring usage, the project is able continually adapt the software to a user's changing needs. Thus, monitoring software usage, particularly email event sequences, is important. This paper introduces theory and design of stream sequence-mining for UI event streams.