Flow structure transition in thermal vibrational convection

This study investigates the effect of vibration on the flow structure transitions in thermal vibrational convection (TVC) systems, which occur when a fluid layer with a temperature gradient is excited by vibration. Direct numerical simulations of TVC in a two-dimensional enclosed square box were performed over a range of dimensionless vibration amplitudes $0.001 \le a \le 0.3$ and angular frequencies $10^{2} \le \omega \le 10^{7}$, with a fixed Prandtl number of 4.38. The flow visualisation shows the transition behaviour of flow structure upon the varying frequency, characterising three distinct regimes, which are the periodic-circulation regime, columnar regime and columnar-broken regime. Different statistical properties are distinguished from the temperature and velocity fluctuations at the boundary layer and mid-height. Upon transition into the columnar regime, columnar thermal coherent structures are formed, in contrast to the periodic oscillating circulation. These columns are contributed by merging of thermal plumes near the boundary layer, and the resultant thermal updrafts remain at almost fixed lateral position, leading to a decrease in fluctuations. We further find that the critical point of this transition can be described nicely by the vibrational Rayleigh number $Ra_\mathrm{vib}$. As the frequency continues to increase, entering the so-called columnar-broken regime, the columnar structures are broken, and eventually the flow state becomes a large-scale circulation, characterised by a sudden increase in fluctuations. Finally, a phase diagram is constructed to summarise the flow structure transition over a wide range of vibration amplitude and frequency parameters.Comment: 14 pages, 9 figure

Approach to Higher Wheat Yield in the Huang-Huai Plain: Improving Post-anthesis Productivity to Increase Harvest Index

Both increased harvest index (HI) and increased dry matter (DM) are beneficial to yield; however, little is known about the priority of each under different yield levels. This paper aims to determine whether HI or DM is more important and identify the physiological attributes that act as indicators of increased yield. Two field experiments involving different cultivation patterns and water-nitrogen modes, respectively, were carried out from 2013 to 2016 in Huang-Huai Plain, China. Plant DM, leaf area index (LAI), and radiation interception (RI) were measured. Increased yield under low yield levels <7500 kg ha-1 was attributed to an increase in both total DM and HI, while increases under higher yield levels >7500 kg ha-1 were largely dependent on an increase in HI. Under high yield levels, HI showed a significant negative correlation with total DM and a parabolic relationship with net accumulation of DM during filling. Higher net accumulation of DM during filling helped slow down the decrease in HI, thereby maintaining a high value. Moreover, net DM accumulation during filling was positively correlated with yield, while post-anthesis accumulation showed a significant linear relationship with leaf area potential (LAP, R2 = 0.404–0.526) and radiation interception potential (RIP, R2 = 0.452–0.576) during grain filling. These findings suggest that the increase in LAP and RIP caused an increase in net DM accumulation after anthesis. Under DM levels >13,000 kg ha-1 at anthesis, maintaining higher LAI and RI in lower layers during grain formation contributed to higher yield. Furthermore, the ratio of upper- to lower-layer RI showed a second-order curve with yield during filling, with an increase in the optimal range with grain development. Pre-anthesis translocation amount, translocation ratios and contribution ratios also showed second-order curves under high yield levels, with optimal values of 3000–4500 kg ha-1, 25–35, and 30–50%, respectively. These results confirm the importance of HI in improving the yield, thereby providing a theoretical basis for wheat production in the Huang-Huai Plain

Impact of an Innovative Financing and Payment Model on Tuberculosis Patients’ Financial Burden: is Tuberculosis Care More Affordable for the Poor?

Background: In response to the high financial burden of health services facing tuberculosis (TB) patients in China, the China-Gates TB project, Phase II, has implemented a new financing and payment model as an important component of the overall project in three cities in eastern, central and western China. The model focuses on increasing the reimbursement rate for TB patients and reforming provider payment methods by replacing fee-for-service with a case-based payment approach. This study investigated changes in out-of-pocket (OOP) health expenditure and the financial burden on TB patients before and after the interventions, with a focus on potential differential impacts on patients from different income groups

Genome and pan-genome assembly of asparagus bean (Vigna unguiculata ssp. sesquipedialis) reveal the genetic basis of cold adaptation

Asparagus bean (Vigna unguiculata ssp. sesquipedialis) is an important cowpea subspecies. We assembled the genomes of Ningjiang 3 (NJ, 550.31 Mb) and Dubai bean (DB, 564.12 Mb) for comparative genomics analysis. The whole-genome duplication events of DB and NJ occurred at 64.55 and 64.81 Mya, respectively, while the divergence between soybean and Vigna occurred in the Paleogene period. NJ genes underwent positive selection and amplification in response to temperature and abiotic stress. In species-specific gene families, NJ is mainly enriched in response to abiotic stress, while DB is primarily enriched in respiration and photosynthesis. We established the pan-genomes of four accessions (NJ, DB, IT97K-499-35 and Xiabao II) and identified 20,336 (70.5%) core genes present in all the accessions, 6,507 (55.56%) variable genes in two individuals, and 2,004 (6.95%) unique genes. The final pan genome is 616.35 Mb, and the core genome is 399.78 Mb. The variable genes are manifested mainly in stress response functions, ABC transporters, seed storage, and dormancy control. In the pan-genome sequence variation analysis, genes affected by presence/absence variants were enriched in biological processes associated with defense responses, immune system processes, signal transduction, and agronomic traits. The results of the present study provide genetic data that could facilitate efficient asparagus bean genetic improvement, especially in producing cold-adapted asparagus bean

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures

Abstract Background Protein structure can be described by backbone torsion angles: rotational angles about the N-Cα bond (φ) and the Cα-C bond (ψ) or the angle between Cαi-1-Cαi-Cαi + 1 (θ) and the rotational angle about the Cαi-Cαi + 1 bond (τ). Thus, their accurate prediction is useful for structure prediction and model refinement. Early methods predicted torsion angles in a few discrete bins whereas most recent methods have focused on prediction of angles in real, continuous values. Real value prediction, however, is unable to provide the information on probabilities of predicted angles. Results Here, we propose to predict angles in fine grids of 5° by using deep learning neural networks. We found that this grid-based technique can yield 2–6% higher accuracy in predicting angles in the same 5° bin than existing prediction techniques compared. We further demonstrate the usefulness of predicted probabilities at given angle bins in discrimination of intrinsically disorder regions and in selection of protein models. Conclusions The proposed method may be useful for characterizing protein structure and disorder. The method is available at http://sparks-lab.org/server/SPIDER2/ as a part of SPIDER2 package

LIQA: lifelong blind image quality assessment

The image distortions are complex and dynamically changing in the real-world scenario, due to the fast development of the image processing system. The blind image quality assessment (BIQA) models may encounter the challenge of processing images with distortion types never seen before deployment. However, existing BIQA models generally cannot evolve with unseen distortion types adaptively, which greatly limits the deployment and application of BIQA models in real-world scenarios. To address this problem, we propose a novel Lifelong blind Image Quality Assessment (LIQA) approach, targeting to achieve the lifelong learning of BIQA. Without accessing to previous training data, our proposed LIQA can not only learn new knowledge, but also mitigate the catastrophic forgetting of learned knowledge. Specifically, we adopt the Split-and-Merge distillation strategy to train a single-head network that makes task-agnostic predictions. In the split stage, we first employ a distortion-specific generator to generate pseudo features of each previously seen distortion. Then, we utilize an auxiliary multi-head regression network to keep the response of each distortion. In the merge stage, we replay the pseudo features and use the pseudo labels generated by the auxiliary multi-head network to distill the knowledge of the multiple heads, which can build the final regression single head. Extensive experiments demonstrate that LIQA can perform well in handling both inner-dataset distortion shift and cross-dataset distortion shift. More importantly, our model can achieve stable performance even if the task sequences are long

Additional file 1: of Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures

Supplementary Information for Grid-based Prediction of Torsion Angle Probabilities of Protein Backbone and Its Application to Discrimination of Protein Intrinsic Disorder Regions and Selection of Model Structures. Figure S1: Average GDT-TS scores of top 1 server models for different methods on the CASP11 dataset (CASP11MOD). Figure S2: Scatter plot for DFIRE energy scores and GDT-TS score for target T0848. Blue line is the regression line between DFIRE energy scores sand GDT-TS scores. Correlation coefficient is − 0.04. Figure S3: Scatter plot for RWplus energy scores and GDTTS score for target T0848. Blue line is the regression line between RWplus energy scores sand GDTTS scores. Correlation coefficient is − 0.03. Figure S4: Scatter plot for M2-φ energy scores and GDT-TS score for target T0848. Blue line is the regression line between M2-φ energy scores sand GDT-TS scores. Correlation coefficient is 0.42. Figure S5: Scatter plot for M2-ψ energy scores and GDT-TS score for target T0848. Blue line is the regression line between M2-ψ energy scores sand GDT-TS scores. Correlation coefficient is 0.49. Figure S6: Scatter plot for M2-θ energy scores and GDT-TS score for target T0848. Blue line is the regression line between M2-θ energy scores sand GDT-TS scores. Correlation coefficient is 0.46. (DOCX 221 kb