Deep learning based prediction on greenhouse crop yield combined TCN and RNN

Funding: This research was supported as part of SMARTGREEN, an Interreg project supported by the North Sea Programme of the European Regional Development Fund of the European Union.Peer reviewedPublisher PD

Research on institutional contradiction and legitimacy crisis of internet car-hailing platform with the Grounded Theory

Based on the case of DiDi travel platform event, we used the grounded theory to analyze the legitimacy crisis occurrence mechanism of internet car-hailing platforms organizational field. We found that, the institutional contradiction between public welfare logic and profit logic in the process of DiDi’s Institutional Entrepreneurship leads to the legitimacy crisis; the crisis event causes the government\u27s supervision, media attention and consumer resistance, which ultimately leads to the deterioration and spillover of legitimacy crisis from DiDi to the whole internet car-hailing platforms organizational field

Comparative transcriptome analysis reveals resistance-related genes and pathways in Musa acuminata banana 'Guijiao 9' in response to Fusarium wilt.

Fusarium wilt caused by Fusarium oxysporum f. sp. cubense (Foc), is one of the most devastating diseases in bananas resulting in significant loss of Cavendish bananas production worldwide. Here we show the agronomic traits and the resistance of 'Guijiao 9' in the field trials from 2012 to 2017. And then we dissect and compare the transcriptome response from these two cultivars (cv. 'Guijiao 9' and cv. Williams) in an attempt to understand the molecular basis that contribute to the enhanced Foc tropical race 4 (Foc-TR4) resistance. 'Guijiao 9' is a Cavendish cultivar with strong resistance to Foc-TR4, which was reflected in a lower disease severity and incidence in glasshouse and field trails, when compared to the susceptible cultivar Williams. Gene expression profiles of 'Guijiao 9' and Williams were captured by performing RNA-Seq analysis on 16 biological samples collected over a six day period post inoculation with Foc-TR4. Transcriptional reprogramming in response to Foc-TR4 was detected in both genotypes but the response was more drastic in 'Guijiao 9' than in Williams. Specific genes involved in plant-pathogen interaction and defense signaling including MAPK, calcium, salicylic acid, jasmonic acid and ethylene pathways were analyzed and compared between 'Guijiao 9' and Williams. Genes associated with defense-related metabolites synthesis such as NB-LRR proteins, calmodulin-binding protein and phenylpropanoids biosynthesis genes were significantly up-regulated in 'Guijiao 9' resistant to Foc-TR4 infection. Taken together, this study highlights the important roles of plant hormone regulation and defense gene activation in mediating resistance in 'Guijiao 9'

Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization

Investigation into the Influence of Physician for Treatment Based on Syndrome Differentiation

Background. The characteristics of treatment based on syndrome differentiation (TBSD) cause great challenges to evaluate the effectiveness of the clinical methods. Objectives. This paper aims to evaluate the influence of physician to personalized medicine in the process of TBSD. Methods. We performed a randomized, triple-blind trial involving patients of primary insomnia treated by 3 physicians individually and independently. The patients (n=30) were randomly assigned to receive treatments by the 3 physicians for every visit. However, they always received the treatment, respectively, prescribed by the physician at the first visit. The primary outcome was evaluated, respectively, by the Pittsburgh Sleep Quality Index (PSQI) and the TCM symptoms measuring scale. The clinical practices of the physicians were recorded at every visit including diagnostic information, syndrome differentiation, treating principles, and prescriptions. Results. All patients in the 3 groups (30 patients) showed significant improvements (>66%) according to the PSQI and TCM symptoms measuring scale. Conclusion. The results indicate that although with comparable effectiveness, there exist significant differences in syndrome differentiation, the treating principles, and the prescriptions of the approaches used by the 3 physicians. This means that the physician should be considered as an important factor for individualized medicine and the related TCM clinical research

A systematic review of studies on stress during the COVID-19 pandemic by visualizing their structure through COOC, VOS viewer, and Cite Space software

BackgroundThe COVID-19 epidemic generated different forms of stress. From this period, there has been a remarkable increase in the quantity of studies on stress conducted by scholars. However, few used bibliometric analyses to focus on overall trends in the field.PurposeThis study sought to understand the current status and trends in stress development during COVID-19, as well as the main research drives and themes in this field.Methods2719 publications from the Web of Science(WOS) core repository on stress during COVID-19 were analyzed by utilizing Co-Occurrence (COOC), VOS viewer, and Cite Space bibliometric software. The overall features of research on stress during COVID-19 were concluded by analyzing the quantity of publications, keywords, countries, and institutions.ResultsThe results indicated that the United States had the largest number of publications and collaborated closely with other countries with each other. University of Toronto was the most prolific institution worldwide. Visualization and analysis demonstrated that the influence of stress during COVID-19 on the work, life, mental and spiritual dimensions is a hot research topic. Among other things, the frequency of each keyword in research on stress during COVID-19 increased from 2021 to 2022, and the researchers expanded their scope and study population; the range of subjects included children, nurses, and college students, as well as studies focusing on different types of stress, and emphasizing the handling of stress.ConclusionOur findings reveal that the heat of stress research during COVID-19 has declined, and the main research forces come from the United States and China. Additionally, subsequent research should concern more on coping methods with stress, while using more quantitative and qualitative studies in the future

Inhibitory effect of small interfering RNA on dengue virus replication in mosquito cells

<p>Abstract</p> <p>Background</p> <p>Dengue viruses (DENs) are the wildest transmitted mosquito-borne pathogens throughout tropical and sub-tropical regions worldwide. Infection with DENs can cause severe flu-like illness and potentially fatal hemorrhagic fever. Although RNA interference triggered by long-length dsRNA was considered a potent antiviral pathway in the mosquito, only limited studies of the value of small interfering RNA (siRNA) have been conducted.</p> <p>Results</p> <p>A 21 nt siRNA targeting the membrane glycoprotein precursor gene of DEN-1 was synthesized and transfected into mosquito C6/36 cells followed by challenge with DEN. The stability of the siRNA in cells was monitored by flow cytometry. The antiviral effect of siRNA was evaluated by measurement of cell survival rate using the MTT method and viral RNA was quantitated with real-time RT-PCR. The presence of cells containing siRNA at 0.25, 1, 3, 5, 7 days after transfection were 66.0%, 52.1%, 32.0%, 13.5% and 8.9%, respectively. After 7 days incubation with DEN, there was reduced cytopathic effect, increased cell survival rate (76.9 ± 4.5% <it>vs </it>23.6 ± 14.6%) and reduced viral RNA copies (Ct value 19.91 ± 0.63 <it>vs </it>14.56 ± 0.39) detected in transfected C6/36 cells.</p> <p>Conclusions</p> <p>Our data showed that synthetic siRNA against the DEN-1 membrane glycoprotein precursor gene effectively inhibited DEN-1 viral RNA replication and increased C6/36 cell survival rate. siRNA may offer a potential new strategy for prevention and treatment of DEN infection.</p

Studies of evolutionary algorithms for the reduced Tomgro model calibration for modelling tomato yields

The reduced Tomgro model is one of the popular biophysical models, which can reflect the actual growth process and model the yields of tomato-based on environmental parameters in a greenhouse. It is commonly integrated with the greenhouse environmental control system for optimally controlling environmental parameters to maximize the tomato growth/yields under acceptable energy consumption. In this work, we compare three mainstream evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and differential evolutionary (DE)) for calibrating the reduced Tomgro model, to model the tomato mature fruit dry matter (DM) weights. Different evolutionary algorithms have been applied to calibrate 14 key parameters of the reduced Tomgro model. And the performance of the calibrated Tomgro models based on different evolutionary algorithms has been evaluated based on three datasets obtained from a real tomato grower, with each dataset containing greenhouse environmental parameters (e.g., carbon dioxide concentration, temperature, photosynthetically active radiation (PAR)) and tomato yield information at a particular greenhouse for one year. Multiple metrics (root mean square errors (RMSEs), relative root mean square errors (r-RSMEs), and mean average errors (MAEs)) between actual DM weights and model-simulated ones for all three datasets, are used to validate the performance of calibrated reduced Tomgro model