The plant-specific WUSCHEL-related homeobox (WOX) genes are crucial for plant growth and development.  Here, we systematically identified the MdWOX gene family in apple at the genome-wide level, and analyzed the phylogenetic relationships, conserved motifs, gene structure, and syntenic relationships of the MdWOX genes.  A total of 18 MdWOX genes were identified and phylogenetic analysis placed them into three clades.  The phylogenetic relationships among the WOXs were further supported by the analyses of gene structure and conserved motifs.  Chromosomal distribution and synteny analysis revealed that whole-genome and segmental duplications have played key roles in MdWOX gene family expansion.  Moreover, the MdWOX genes exhibit tissue-specific expression patterns and MdWOX4a, MdWOX4b, MdWOX5b, MdWOX11/12a, and MdWOX11/12b may play essential roles in adventitious root development.  The adventitious rooting ability was enhanced in MdWOX4b transgenic tobacco lines.  The results of this study provide useful information for future functional studies on MdWOXs in the development of apple rootstocks.  

There are 41 members of the CCT (CO, CO-like, and TOC1) domain-containing gene family in rice, which are divided into three subfamilies: COL (CONSTANS-like), CMF (CCT motif family), and PRR (pseudoresponse regulator).  The first flowering gene to be isolated by map-based cloning, Heading date 1 (Hd1), which is the orthologue of CO in rice, belongs to COL.  The central regulator of plant development, Ghd7, belongs to CMF.  The major role in controlling rice distribution to high latitudes, Ghd7.1/PRR37, belongs to PRR.  Both of Hd1, Ghd7 and Ghd7.1 simultaneously control grain number, plant height, and the heading date.  To date, 13 CCT family genes from these three subfamilies have been shown to regulate flowering.  Some of them have pleiotropic effects on grain yield, plant height, and abiotic stresses, and others function as circadian oscillators.  There are two independent photoperiod flowering pathways that are mediated by GI-Hd1-Hd3a/RFT and GI-Ehd1-Hd3a/RFT in rice.  CCT family genes are involved in both pathways.  The latest study reveals that protein interaction between Hd1 and Ghd7 integrates the two pathways.  CCT family genes are rich in natural variation because rice cultivars have been subjected to natural and artificial selection for different day lengths in the process of domestication and improvement.  Alleles of several crucial CCT family genes such as Hd1, Ghd7, and Ghd7.1 exhibit geographic distribution patterns and are highly associated with yield potentials.  In addition, CCT family genes are probably involved in the responses to abiotic stress, which should be emphasized in future work.  In general, CCT family genes play important roles in regulating flowering, plant growth, and grain yield.  The functional identification and elucidation of the molecular mechanisms of CCT family genes would help construct a flowering regulatory network and maximize their contribution to rice production.

Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution.  This indirectly leads to variations in the leaf potassium content (LKC, %) at different leaf positions.  However, owing to the interaction between light and leaf age, leaf sensitivity at different positions to this change varies, including the reflection and absorption of the spectrum.  How to selecting the optimal monitoring leaf position is an important factor in quickly and accurately evaluation of cotton LKC using spectral remote sensing technology.  Therefore, this study proposes a comprehensive multileaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom.  This is aimed at achieving an accurate estimation of cotton LKC and optimizing the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, ... , n), during the cotton budding, flowering, and boll setting stages.  Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated.  Additionally, the optimal range of the dominant leaf position for monitoring was determined.  Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were used to establish LKC estimation models for single leaf and multileaf positions.  The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom, and the average LKC of cotton reaches its maximum value at flowering stage.  The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum.  The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively.  Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with The coefficient of determination of the validation set (R²val) of 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) of 0.168, 0.197, and 0.191, respectively.  The multileaf position LKC estimation model was constructed by EWM with R²val of 0.887, 0.728, and 0.703, and RMSEval of 0.134, 0.172, and 0.209, respectively.  In contrast, the newly developed multileaf position comprehensive estimation model yielded superior results, improving the stability of the model on the basis of high accuracy, especially during the budding and flowering stages.  These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.