Vanceblack0704

w DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https//github.com/tjgu/miTAR . Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.

The challenges imposed by ageing populations will confront health care systems in the years to come. Hospital owners are concerned about the increasing number of acute admissions of older citizens and preventive measures such as integrated care models have been introduced in primary care. Yet, acute admission can be appropriate and lifesaving, but may also in itself lead to adverse health outcome, such as patient anxiety, functional loss and hospital-acquired infections. Timely identification of older citizens at increased risk of acute admission is therefore needed. We present the protocol for the PATINA study, which aims at assessing the effect of the 'PATINA algorithm and decision support tool', designed to alert community nurses of older citizens showing subtle signs of declining health and at increased risk of acute admission. This paper describes the methods, design and intervention of the study.

We use a stepped-wedge cluster randomized controlled trial (SW-RCT). The PATINA algorithm and decision smaking. This may increase preventive measures in primary care and reduce use of secondary health care. Further, the study will increase our knowledge of barriers and facilitators to implementing algorithms and decision support in a community care setup.

ClinicalTrials.gov , identifier NCT04398797 . Registered 13 May 2020.

ClinicalTrials.gov , identifier NCT04398797 . Registered 13 May 2020.The adaptive array invariant developed for source-range estimation in shallow water can incorporate the propagation-angle dependence of the waveguide invariant for an ideal waveguide (β=cos2θ) [Byun and Song, J. Acoust. Anacetrapib mouse Soc. Am. 148, 925-933 (2020)]. This paper extends the approach to weakly (adiabatic) range-dependent environments with variable bathymetry, wherein the waveguide invariant is a complex function of the bathymetry between source and receiver as well as the propagation angle in powers of sin2θ. For a given bathymetry, the adaptive array invariant can be implemented in an iterative fashion, and its remarkable performance is demonstrated using a short-aperture vertical array (2.8 m) for a broadband source (0.5-3.5 kHz) towed on a continental slope where the water depth varies from 87.5 to 55 m over a 5-km range.The present study examines the acoustic realization of the English, Japanese, and Spanish /k/ in the productions of two groups of English-Japanese bilinguals [first language (L1) English-second language (L2) Japanese and L1 Japanese-L2 English] and one trilingual group [L1 Spanish-L2 English-third language (L3) Japanese]. With the analysis of voice onset time (VOT) as a proxy for the degree of cross-linguistic influence in each language, this experiment compares the production patterns of L2 and L3 learners of Japanese and explores the effects of language mode and cognate status on the speech patterns in each of the languages of these bilingual and trilingual individuals. By manipulating the degree of activation of the target and non-target language(s) with the use of cognates and non-cognates in monolingual, bilingual, and trilingual experimental sessions, this study investigates static as well as transient phonetic influence. Even though these bilingual and trilingual speakers produce language-specific VOT patterns for each language, the acoustic analyses also reveal evidence of phonetic convergence as a result of language mode and cognate status. These results show that trilingual speakers are able to maintain language-specific phonological categories in their L1, L2, and L3, overcoming long-term (static) traces of one language influencing the other, despite evidence of short-term (dynamic) cross-linguistic influence.Emotion is a central component of verbal communication between humans. Due to advances in machine learning and the development of affective computing, automatic emotion recognition is increasingly possible and sought after. To examine the connection between emotional speech and significant group dynamics perceptions, such as leadership and contribution, a new dataset (14 group meetings, 45 participants) is collected for analyzing collaborative group work based on the lunar survival task. To establish a training database, each participant's audio is manually annotated both categorically and along a three-dimensional scale with axes of activation, dominance, and valence and then converted to spectrograms. The performance of several neural network architectures for predicting speech emotion are compared for two tasks categorical emotion classification and 3D emotion regression using multitask learning. Pretraining each neural network architecture on the well-known IEMOCAP (Interactive Emotional Dyadic Motion Capture) corpus improves the performance on this new group dynamics dataset. For both tasks, the two-dimensional convolutional long short-term memory network achieves the highest overall performance. By regressing the annotated emotions against post-task questionnaire variables for each participant, it is shown that the emotional speech content of a meeting can predict 71% of perceived group leaders and 86% of major contributors.Unmanned aerial vehicle (UAV) technologies are rapidly advancing due to the unlimited number of applications from parcel delivery to people transportation. As the UAV market expands, community noise impact will become a significant problem for public acceptance. Compact drone architectures based on contra-rotating propellers bring significant benefits in terms of aerodynamic performance and redundancy to ensure vehicle control in case of component failure. However, contra-rotating propellers are severely noisy if not designed appropriately. In the framework of a perception-influenced design approach, this paper investigates the optimal rotor spacing distance configuration to minimise noise annoyance. On the basis of a series of psychoacoustic metrics (i.e., loudness, fluctuation strength, roughness, sharpness, and tonality) and psychoacoustic annoyance (PA) models, the optimal rotor axial separation distance (expressed as a function of propeller blade diameter) is at a range from 0.2 to 0.4. This paper also discusses the performance of currently available psychoacoustic models to predict propeller noise annoyance and defines further work to develop a PA model optimised for rotating systems.