MicroRNAs (miRNAs), a class of small non-coding RNAs, are crucial endogenous gene regulators in a range of animals, including plant-parasitic nematodes.  Meloidogyne graminicola is an obligate sedentary endoparasite of rice and causes significant yield losses.  A number of studies focused on the roles of M. graminicola effectors during the parasitic process; however, how nematode miRNAs regulate its effectors needs elucidating.  In this research, we analyzed a cluster of M. graminicola miRNAs obtained at the second-stage juveniles (J2s) stage that are closely linked to the regulation of M. graminicola effectors.  There are 49 767 105 total clean reads obtained from three libraries.  A total of 233 known miRNAs and 21 novel miRNAs were identified.  Among the known miRNAs, mgr-lin-4, mgr-mir-1, mgr-mir-100, mgr-mir-86, mgr-mir-279, mgr-mir-87, mgr-mir-71, mgr-mir-9, mgr-mir-50, mgr-mir-72, and mgr-mir-34 are the most abundant 11 miRNAs families.  Moreover, the expression levels of selected miRNAs were validated by real-time quantitative PCR.  We hypothesized that these miRNAs might regulate the expression of secreted effectors during the J2s stage to facilitate its infection.  Consistent with this, we found that mgr-mir-9 targets MgPDI, an important M. graminicola effector mRNA.  In addition to that, J2s treated with mgr-mir-9 mimics showed down-regulation of MgPDI expression and reduced reproductive ability, alluding mgr-mir-9 is involved in nematode infection.  These results provide novel insight into the regulatory functions of M. graminicola miRNAs during the infection and identify miRNAs and their effector targets as potential key management targets to limit parasite survival during the early stages of infection.

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.  Plant height (H), stem diameter (SD), leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.  However, the combined effect of temperature and light on maize growth is rarely considered in crop growth models.  Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H, SD, LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.  Either the accumulative growing degree-days (AGDD), helio thermal units (HTU), photothermal units (PTU) or photoperiod thermal units (PPTU, first proposed here) was used as a single driving factor in the models; or AGDD was combined with either accumulative actual solar hours (ASS), accumulative photoperiod response (APR, first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.  The model performances were evaluated using seven statistical indicators and a global performance index.  The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.  Among the four single factor-driven models, the overall performance of the Mlog_PTU Model was the best, followed by the Mlog_AGDD Model.  The Mlog_PPTU Model was better than the Mlog_AGDD Model in simulating SD and LAI.  Among the 10 models, the overall performance of the Mlog_AGDD–APR Model was the best, followed by the Mlog_AGDD–ASS Model.  Specifically, the Mlog_AGDD–APR Model performed the best in simulating H and LAI, while the Mlog_AGDD–ADL and Mlog_AGDD–ASS models performed the best in simulating SD and DM, respectively.  In conclusion, the modified logistic growth equations with AGDD and either APR, ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.