Ionic cluster effect in suppression on superconductivity in Ni- and Co-doped YBCO systems

We adopted the x-ray diffraction, oxygen contents, positron annihilation technology as well as simulation methods to investigate systemically YBa₂Cu₃–x(Ni,Co)xO₇–δ (x = 0–0.5). The simulated results show that ions distribute in dispersive form in little doped concentration. As doped concentration increases, ions combine into clusters in the crystal lattice. The calculated results and oxygen contents, together with the impure phases and the local electron density ne, show the ionic cluster effect, which not only causes the local electron density to reach the saturation, but also suppress the superconductivity significantly

Physiological and Transcriptome Analyses Reveal Short-Term Responses and Formation of Memory Under Drought Stress in Rice

In some plants, exposure to stress can induce a memory response, which appears to play an important role in adaptation to recurrent stress environments. However, whether rice exhibits drought stress memory and the molecular mechanisms that might underlie this process have remained unclear. Here, we ensured that rice drought memory was established after cycles of mild drought and re-watering treatment, and studied gene expression by whole-transcriptome strand-specific RNA sequencing (ssRNA-seq). We detected 6,885 transcripts and 238 lncRNAs involved in the drought memory response, grouped into 16 distinct patterns. Notably, the identified genes of dosage memory generally did not respond to the initial drought treatment. Our results demonstrate that stress memory can be developed in rice under appropriate water deficient stress, and lncRNA, DNA methylation and endogenous phytohormones (especially abscisic acid) participate in rice short-term drought memory, possibly acting as memory factors to activate drought-related memory transcripts in pathways such as photosynthesis and proline biosynthesis, to respond to the subsequent stresses

Effects of elevated CO2 and temperature on yield and fruit quality of strawberry (Fragaria × ananassa Duch.) at two levels of nitrogen application.

We investigated if elevated CO(2) could alleviate the negative effect of high temperature on fruit yield of strawberry (Fragaria × ananassa Duch. cv. Toyonoka) at different levels of nitrogen and also tested the combined effects of CO(2), temperature and nitrogen on fruit quality of plants cultivated in controlled growth chambers. Results show that elevated CO(2) and high temperature caused a further 12% and 35% decrease in fruit yield at low and high nitrogen, respectively. The fewer inflorescences and smaller umbel size during flower induction caused the reduction of fruit yield at elevated CO(2) and high temperature. Interestingly, nitrogen application has no beneficial effect on fruit yield, and this may be because of decreased sucrose export to the shoot apical meristem at floral transition. Moreover, elevated CO(2) increased the levels of dry matter-content, fructose, glucose, total sugar and sweetness index per dry matter, but decreased fruit nitrogen content, total antioxidant capacity and all antioxidant compounds per dry matter in strawberry fruit. The reduction of fruit nitrogen content and antioxidant activity was mainly caused by the dilution effect of accumulated non-structural carbohydrates sourced from the increased net photosynthetic rate at elevated CO(2). Thus, the quality of strawberry fruit would increase because of the increased sweetness and the similar amount of fruit nitrogen content, antioxidant activity per fresh matter at elevated CO(2). Overall, we found that elevated CO(2) improved the production of strawberry (including yield and quality) at low temperature, but decreased it at high temperature. The dramatic fluctuation in strawberry yield between low and high temperature at elevated CO(2) implies that more attention should be paid to the process of flower induction under climate change, especially in fruits that require winter chilling for reproductive growth

Knowledge-Aided Doppler Beam Sharpening Super-Resolution Imaging by Exploiting the Spatial Continuity Information

This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm

Statistical modeling of gut microbiota for personalized health status monitoring

Abstract Background The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host’s health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated. Methods We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets. Results We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ( $$\rho = 0.07$$ ρ = 0.07 ) and species richness ( $$\rho = 0.44$$ ρ = 0.44 ) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ( $$\rho = 0.12$$ ρ = 0.12 ), species richness ( $$\rho = 0.46$$ ρ = 0.46 ), and functional redundancy ( $$\rho = - 0.16$$ ρ = - 0.16 ). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases. Conclusions Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstrac

Total fruit dry weight (a), total fruit number (b) and fruit grades (c, d, e) of strawberry plants cultivated under different conditions (mean ± SD, n = 4).

<p>The berries were graded in three size classes depending on fruit dry weight (FDW; FDW <0.4 g, grade 1; 0.4≤ FDW ≤0.7 g, grade 2; FDW >0.7 g, grade 3). The frequency distribution of grade 1, 2 and 3 is showed in figures c, d and e, respectively. Bars indicate standard deviation, while ^* and ^** indicate significant differences at <i>P</i><0.05 and 0.01, respectively.</p

MGLM analysis of treatment (CO₂, temperature and nitrogen) main effects and their interactions on AC, TP, TF, DPPH, ABTS and FNC of strawberry fruits for plants cultivated at ambient (360 ppm) and elevated (720 ppm) CO₂, high and low temperature, and high and low nitrogen^a.

a<p>Data are expressed as F values, and *, **, ***, **** indicate P<0.05, 0.01, 0.001 and 0.0001, respectively. Abbreviations are: FW- fresh weight; Temp- Temperature; N- Nitrogen; 3-Way- CO₂×Temperature<b>×</b>Nitroge.</p

Correlations between fruit dry weight and total achene number (TAN) of strawberry fruits grown in different conditions^a.

a<p>the linear regression: y  =  a x + b, a-slope of linear regression, b-increment of linear regression, r-correlation coefficient.</p>**<p>indicate P<0.01.</p