A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

We aimed to understand the relation of photosynthetic rate (A) with g(s) and electron transport rate (ETR) in species of great taxonomic range and light adaptation capability during photosynthetic light induction. We studied three woody species (Alnus formosana, Ardisia crenata and Ardisia cornudentata) and four fern species (Pyrrosia lingus, Asplenium antiquum, Diplazium donianum and Archangiopteris somai) with different light adaptation capabilities. Pot-grown materials received 100 and/or 10% sunlight according to their light adaptation capabilities. At least 4 months after light acclimation, CO(2) and H(2)O exchange and chlorophyll fluorescence were measured simultaneously by equipment in the laboratory. In plants adapted or acclimated to low light, dark-adapted leaves exposed to 500 or 2000 µmol m(-2) s(-1) photosynthetic photon flux (PPF) for 30 min showed low gross photosynthetic rate (P(g)) and short time required to reach 90% of maximum P(g) (). At the initiation of illumination, two broad-leaved understory shrubs and the four ferns, especially ferns adapted to heavy shade, showed higher stomatal conductance (g(s)) than pioneer tree species; materials with higher g(s) had short at both 500 and 2000 µmol m(-2) s(-1) PPF. With 500 or 2000 µmol m(-2) s(-1) PPF, the g(s) for the three woody species increased from 2 to 30 min after the start of illumination, but little change in the g(s) of the four ferns. Thus, P(g) and g(s) were not correlated for all material measured at the same PPF and induction time. However, P(g) was positively correlated with ETR, even though CO(2) assimilation may be influenced by stomatal, biochemical and photoinhibitory limitations. In addition, was closely related to time required to reach 90% maximal ETR for all materials and with two levels of PPF combined. Thus, ETR is a good indicator for estimating the light induction of photosynthetic rate of species, across a wide taxonomic range and light adaptation and acclimation capability.

Background: Vitamin D deficiency is common in the general population worldwide, and the prevalence and severity of vitamin D deficiency increase in critically ill patients. The prevalence of vitamin D deficiency in a community-based cohort in Northern Taiwan was 22.4%. This multicenter cohort study investigated the prevalence of vitamin D deficiency and associated factors in critically ill patients in Northern Taiwan.Methods: Critically ill patients were enrolled and divided into five groups according to their length of stay at intensive care units (ICUs) during enrolment as follows: group 1, <2 days with expected short ICU stay; group 2, <2 days with expected long ICU stay; group 3, 3-7 days; group 4, 8-14 days; and group 5, 15-28 days. Vitamin D deficiency was defined as a serum 25-hydroxyvitamin D (25(OH)D) level < 20 ng/ml, and severe vitamin D deficiency was defined as a 25(OH)D level < 12 ng/ml. The primary analysis was the prevalence of vitamin D deficiency. The exploratory analyses were serial follow-up vitamin D levels in group 2, associated factors for vitamin D deficiency, and the effect of vitamin D deficiency on clinical outcomes in critically ill patients.Results: The prevalence of vitamin D deficiency was 59% [95% confidence interval (CI) 55-62%], and the prevalence of severe vitamin D deficiency was 18% (95% CI 15-21%). The median vitamin D level for all enrolled critically ill patients was 18.3 (13.7-23.9) ng/ml. In group 2, the median vitamin D levels were <20 ng/ml during the serial follow-up. According to the multivariable analysis, young age, female gender, low albumin level, high parathyroid hormone (PTH) level, and high sequential organ failure assessment (SOFA) score were significantly associated risk factors for vitamin D deficiency. Patients with vitamin D deficiency had longer ventilator use duration and length of ICU stay. However, the 28- and 90-day mortality rate were not associated with vitamin D deficiency.Conclusions: This study demonstrated that the prevalence of vitamin D deficiency is high in critically ill patients. Age, gender, albumin level, PTH level, and SOFA score were significantly associated with vitamin D deficiency in these patients.

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Securing access to data, applied to mobile service applications with temporal and spatial controlling, requires constructing innovative definitions with temporal and spatial limitations for an access-control system. To cope with the temporal and spatial requirements, we propose a generalized Temporal and Spatial RBAC (TSRBAC) model. In the TSRBAC model, temporal-period and spatial-location based entities are used to constrain the permissions of objects, user positions, and geographically bounded roles. Furthermore, we also present temporal and spatial relations of Temporal and Spatial Separation of Duties (TSSSD), Temporal and Spatial Dynamic Separation of Duties (TSDSD) constraints in the TSRBAC model