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Abstract Plant-sucking insects have intricate associations with a diverse array of microorganisms to facilitate their adaptation to specific ecological niches. The midgut of phytophagous true bugs is generally structured into four distinct compartments to accommodate their microbiota. Nevertheless, there is limited understanding regarding the origins of these gut microbiomes, the mechanisms behind microbial community assembly, and the interactions between gut microbiomes and their insect hosts. In this study, we conducted a comprehensive survey of microbial communities within the midgut compartments of a bean bug Riptortus pedestris, soybean plant, and bulk soil across 12 distinct geographical fields in China, utilizing high-throughput sequencing of the 16 S rRNA gene. Our findings illuminated that gut microbiota of the plant-sucking insects predominantly originated from the surrounding soil environment, and plants also play a subordinate role in mediating microbial acquisition for the insects. Furthermore, our investigation suggested that the composition of the insect gut microbiome was probably shaped by host selection and/or microbe-microbe interactions at the gut compartment level, with marginal influence from soil and geographical factors. Additionally, we had unveiled a noteworthy dynamic in the acquisition of core bacterial taxa, particularly Burkholderia, which were initially sourced from the environment and subsequently enriched within the insect midgut compartments. This bacterial enrichment played a significant role in enhancing insect host reproduction. These findings contribute to our evolving understanding of microbiomes within the insect-plant-soil ecosystem, shedding additional light on the intricate interactions between insects and their microbiomes that underpin the ecological significance of microbial partnerships in host adaptation.

Background Methods Results Conclusions Nutritional risk index (NRI) and carcinoembryonic antigen (CEA) are useful prognostic markers in colorectal cancer (CRC); however, the prognostic value of a combination of the NRI and CEA, namely, the NRI and CEA score (NCS), needs further investigation.Stage I‐III CRC patients were collected and then divided into three subgroups by counting the NCS: NCS 1: high NRI with normal CEA; NCS 2: high NRI with elevated CEA or low NRI with normal CEA; and NCS 3: low NRI with elevated CEA. The differences in outcome, counted as disease‐free survival (DFS) and overall survival (OS), were tested among the subgroups.A total of 285 patients were enrolled, with 108 in NCS 1, 118 in NCS 2 and 59 in NCS 3. Patient features, including age, tumour deposit, T stage, N stage and TNM stage, were significantly different in the NCS subgroups. Both the DFS (log‐rank = 26.06, P<0.001) and OS (log‐rank = 39.10, P<0.001) were significant in different NCS subgroups, even in maximum tumour diameter ≤4 cm cases (DFS: log‐rank = 21.42, P<0.001; OS: log‐rank = 30.95, P<0.001), and NCS 1 patients displayed the best outcome compared with the rest of the subgroups. NCS was also found to be an independent risk factor for both DFS and OS.NCS was a useful prognostic indicator in stages I–III CRC patients. [ABSTRACT FROM AUTHOR]

Straw returning has gradually been adopted as an effective approach to address the serious degradation of farmland. However, the carbon/nitrogen (C/N) ratio of rice straw is generally too high for microorganisms to decompose the organic materials and release nutrients, which may minimize the benefits of straw returning to the agricultural production system. This study aimed to investigate the effects of straw returning on rice production and propose optimum nitrogen (N) management for early rice production under a straw returning system. The total N fertilizer that was evaluated was 165 kg N ha-1, urea (46% N), applied in different proportions in three stages of rice cultivation: basal, tillering, and panicle. Using no straw returning with the N fertilizer ratio of basal:tillering:panicle = 5:2:3 treatment (T1) as the control, four different N fertilizer ratios of basal:tillering:panicle, including 5:2:3 (T2), 5:2:2 (T3), 5:4:1 (T4), and 5:5:0 (T5) were set under straw returning. The return of straw decreased the available N in the soil at the tillering stage, and impeded root growth and the crop canopy from establishing, which decreased the effective panicles by 10.1% compared with that of T1, limiting the increases in rice grain yield. Increasing the N fertilizer ratio 10–20% (T3 and T4) at the tillering stage effectively increased the content of soil ammonium and nitrate nitrogen, improved the root growth, and increased the root activities by 16.0–40.5% at the tillering stage. As a result, the effective panicle number increased by 5.1–16.2%. Among these, T4 treatment maximized the benefits of straw returning the most. Additionally, increasing the N fertilizer ratio at the tillering stage increased the shoot uptake across the early rice growing season and synchronized crop N uptake with the accumulation of carbon assimilates, which enhanced the crop growth rate and increased the rice yield by 13.5–25.1%. It is concluded that increasing the N fertilizer ratio by 20% at the tillering stage is a promising strategy to increase the availability of N in the phases of high demand for this nutrient. [ABSTRACT FROM AUTHOR]

Abstract The long-term use of a piezoelectric smart structure make it difficult to judge whether the structure or piezoelectric lead zirconate titanate (PZT) is damaged when the signal changes. If the sensor fault occurs, the cases and degrees of the fault are unknown based on the electromechanical impedance method. Therefore, after the principal component analysis (PCA) of six characteristic indexes, a two-component solution that could explain 99.2% of the variance in the original indexes was obtained to judge whether the damage comes from the PZT. Then LibSVM was used to make an effective identification of four sensor faults (pseudo soldering, debonding, wear, and breakage) and their three damage degrees. The result shows that the identification accuracy of damaged PZT reached 97.5%. The absolute scores of PCA comprehensive evaluation for structural damages are less than 0.5 while for sensor faults are greater than 0.6. By comparing the scores of the samples under unknown conditions with the set threshold, whether the sensor faults occur is effectively judged; the intact and 12 possible damage states of PZT can be all classified correctly with the model trained by LibSVM. It is feasible to use LibSVM to classify the cases and degrees of sensor faults.