Association of plant-based diet index with sleep quality in middle-aged and older adults: The Healthy Dance Study.

OBJECTIVES: To investigate the association between plant-based diet indices and sleep quality in Chinese middle-aged and older adults. METHODS: The study included 2424 participants aged 45 years and older. Dietary data were collected using a semi-quantitative food frequency questionnaire, and sleep quality was assessed by the Pittsburgh Sleep Quality Index scale. Plant-based diet was categorized based on 3 indices (score range, 17-85) covering 17 food groups: the overall plant-based diet index, healthful plant-based diet index, and unhealthful plant-based diet index. The associations between these plant-based diet indices and sleep quality were examined using logistic and linear regression analyses. RESULTS: After controlling for sociodemographic, lifestyle, and multiple disease-related factors, participants in the highest quartile of the healthful plant-based diet index had 0.55 higher odds of better sleep quality (95% CI: 0.42, 0.72; Ptrend< .001). In contrast, participants in the highest quartile of the unhealthful plant-based diet index had 2.03 higher odds of poor sleep quality (95% CI: 1.51, 2.72; Ptrend< .001). In addition, plant-based diet index and healthful plant-based diet index were inversely associated with Pittsburgh Sleep Quality Index scores, while unhealthful plant-based diet index and Pittsburgh Sleep Quality Index scores were positively associated. CONCLUSIONS: We found unhealthy plant-based diets are significantly associated with poor sleep quality. Adherence to overall plant-based diets, especially healthy plant-based diets, was positively associated with optimal sleep quality.

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.

The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

Technique, outcome and changes in prostate dimensions in patients with urinary retention managed by aquablation.

PURPOSE: This study aimed to investigate the functional and urodynamic outcome of Aquablation in patients with acute urinary retention (AUR) on catheters. METHODS: Men aged 50-70 who failed medical treatment of BPO with AUR failing to wean off urethral catheter were recruited to undergo Aquablation. Individuals were assessed pre-operatively and at 3 and 6 months after surgery. The primary outcome was defined by the success rate of weaning off catheter. Secondary outcomes were measured by a change in prostate size, symptom scores and urodynamic parameters. RESULTS: Twenty patients underwent Aquablation between June 2019 and September 2020. Mean duration of the urethral catheter in-situ was 5.9 ± 4.9 weeks and mean prostate size of the cohort pre-operatively was 60.8 ± 15.8 cc. A second pass Aqaublation treatment was performed in 14 patients. Five patients failed to wean off the catheter on the first attempt after surgery, requiring another attempt 1 week later which were all successful. At 3 months after the operation, a significant reduction in prostate volume was observed (60.8 ± 15.8 cc vs 24.9 ± 10.3 cc, p < 0.001). No change in international index of erectile function (IIEF) was found (baseline: 16.1 ± 5.8; 3-month: 14.9 ± 6.4; p = 0.953). Mean bladder outlet obstruction index was 14.2 ± 23.0 at 6 months upon urodynamic assessment with 75% of patients had a resolution of detrusor overactivity. Reduction in prostate length was found to be more significant than a reduction in width and height after Aquablation (R = 0.693, p = 0.039). CONCLUSION: From the early data of a single centre, Aquablation was shown to provide a consistent improvement in symptoms, uroflowmetry and urodynamic parameters in patient with a urethral catheter. Results from our study suggest that improvement from Aquablation is reproducible in patients with AUR.

Nonparametric inference of interaction laws in systems of agents from trajectory data.

Inferring the laws of interaction in agent-based systems from observational data is a fundamental challenge in a wide variety of disciplines. We propose a nonparametric statistical learning approach for distance-based interactions, with no reference or assumption on their analytical form, given data consisting of sampled trajectories of interacting agents. We demonstrate the effectiveness of our estimators both by providing theoretical guarantees that avoid the curse of dimensionality and by testing them on a variety of prototypical systems used in various disciplines. These systems include homogeneous and heterogeneous agent systems, ranging from particle systems in fundamental physics to agent-based systems that model opinion dynamics under the social influence, prey-predator dynamics, flocking and swarming, and phototaxis in cell dynamics.

Salkowski's reagent test as a primary screening index for functionalities of rhizobacteria isolated from wild dipterocarp saplings growing naturally on medium-strongly acidic tropical peat soil.

Rhizobacteria isolated from wild dipterocarp saplings in Central Kalimantan, Indonesia, were subjected to Salkowski's reagent test, which is often used in detecting indolic substances. Among 69 isolates grown in a low-nitrogen medium supplemented with L-tryptophan (TRP), culture fluids of 29 strains were positive to the test, in which 17 bacteria turned red and other 10 pink. All the red type rhizobacteria actively converted TRP into tryptophol (TOL), while some yielded indole-3-acetic acid (IAA) with TOL production. They also showed a capacity to decompose gallotannin into pyrogallol via gallic acid. On the other hand, an active IAA-producing Serratia sp. CK67, and three Fe-solubilizing Burkholderia spp. CK28, CK43, and Citrobacter sp. CK42, were all involved in pink type rhizobacteria, which were more effective, oxidative TRP-degraders than the red type rhizobacteria. Thus, Salkowski's reagent test should be a useful primary index in the screening of functional rhizobacteria in peatland ecosystem.

Burkholderia vietnamiensis isolated from root tissues of Nipa Palm (Nypa fruticans) in Sarawak, Malaysia, proved to be its major endophytic nitrogen-fixing bacterium.

Root-associating bacteria of the nipa palm (Nypa fruticans), preferring brackish-water affected mud in Sarawak, Malaysia, were investigated. In a comparison of rhizobacterial microbiota between the nipa and the sago (Metroxylon sagu) palm, it was found that the nipa palm possessed a group of Burkholderia vietnamiensis as its main active nitrogen-fixing endophytic bacterium. Acetylene reduction by the various isolates of B. vietnamiensis was constant (44 to 68 nmol h(-1) in ethylene production rate) in soft gel medium containing 0.2% sucrose as sole carbon source, and the bacterium also showed motility and biofilm-forming capacity. This is the first report of endophytic nitrogen-fixing bacteria from nipa palm.