春季低温（LT）已成为制约小麦生长发育的主要非生物胁迫之一。为研究小麦籽粒发育对孕穗期低温胁迫的响应机制，进行了多种分析，包括孕穗期低温处理后小麦籽粒形态观察、淀粉合成酶活性测定以及直链淀粉和支链淀粉含量测定。此外，利用串联质谱标签技术（TMT）进行了蛋白质组学分析。结果表明，低温胁迫后小麦籽粒的饱满度下降。此外，蔗糖合酶（SuS, EC 2.4.1.13）和腺苷二磷酸葡萄糖焦磷酸化酶（AGPase, EC 2.7.7.27）活性显著下降，导致直链淀粉和支链淀粉含量显著降低。通过蛋白质组学分析，共鉴定出509个差异表达蛋白（DEPs）。GO富集分析表明，分子功能中的营养储存库活性蛋白差异倍数最大，并且上调表达的贮藏蛋白（SSP）在籽粒对低温胁迫及后续伤害的响应中起着积极作用。KEGG富集分析表明，低温胁迫降低了蔗糖和淀粉代谢途径中蔗糖磷酸合成酶（SPS）、葡萄糖-1-磷酸腺苷转移酶（glgC)）和β-呋喃果糖苷酶（FFase）等DEPs的表达，从而影响了籽粒淀粉的合成。此外，在内质网途径的蛋白质加工中发现了许多热休克蛋白（HSPs），这些HSPs可以抵抗低温胁迫带来的一些损伤。这些研究结果为阐明春季低温胁迫后小麦产量形成的潜在机理提供了新的理论基础。

氮（N）和钾（K）是水稻生长过程中两种关键的矿质营养元素。准确诊断氮、钾的状况，对水稻在特定生长阶段的合理施肥具有重要意义。因此，我们提出了一种用于在幼穗分化期（EPIS）诊断水稻营养水平的混合模型，它将嵌入注意力机制的卷积神经网络和长短期记忆网络（LSTM）相结合。在为期两年的实验中，该模型在无人机从不同生长阶段的水稻冠层收集的大量序列图像上得到了验证。与 VGG16、AlexNet、GoogleNet、DenseNet 和 inceptionV3 相比，ResNet101 结合 LSTM的模型在黄花占（HHZ，一种籼稻品种）数据集上获得了 83.81% 的最高平均准确率。当在 2021 年的 HHZ 和秀水 134（XS134，一种粳稻品种）数据集上进行测试时，使用 Squeeze-and-Excitation (SE) 增强的 ResNet101-LSTM 模型达到了 85.38% 和 88.38% 的最高准确率，并且通过跨数据集方法，该模型在2022年测试的HHZ和XS134数据集上的平均准确率分别为81.25%和82.50%，表现出良好的泛化能力。我们提出的模型涉及水稻不同生育阶段的动态信息，可以有效地诊断在EPIS 中水稻不同的营养状况，有助于在水稻穗萌发阶段做出合理施肥的实际决策。

全球变暖的主要特征是非对称性增温，即冬春季和夜间增温幅度大于夏秋季和白天的增温幅度。于2019~2020和2020~2021年两个小麦生长季，以扬麦18（YM18）、苏麦188（SM188）、烟农19（YN19）和安农0711（AN0711）为试验材料，采用被动式夜间增温方法，对小麦生育前期进行不同阶段夜间增温处理，即分蘖期至拔节期夜间增温处理（NW_T-J）、拔节期至孕穗期夜间增温处理（NW_J-B）、孕穗期至开花期夜间增温处理（NW_B-A），以不增温为对照（NN），通过小麦干物质积累与转运特性，籽粒蔗糖和淀粉积累特性，研究不同阶段夜间增温对小麦产量形成的影响。结果表明，不同阶段夜间增温通过提高小麦的千粒重以及可孕小穗数从而提高小麦产量，NW_T-J处理下4个品种小麦产量均显著高于NN，半冬性小麦品种YN19和AN0711受增温的影响大于春性小麦品种YM18和SM188。NW_T-J处理通过提高小麦营养生长阶段的生长速率从而增加了小麦开花期和成熟期各器官干物质积累量，且以旗叶和穗部的干物质积累量提升比例较大。NW_T-J处理还提高了小麦灌浆前期和中期的籽粒蔗糖和淀粉含量，从而促进产量的形成。综上所述，分蘖到拔节期夜间增温通过促进小麦花前的生长速率从而提高了小麦的干物质生产能力进而有利于产量的提高。

Aegiliops tauschii is classified into two subspecies: Ae. tauschii ssp. tauschii and Ae. tauschii ssp. strangulata. Novel genetic variations exist in Ae. tauschii ssp. tauschii that can be utilized in wheat improvement. We synthesized a hexaploid wheat genotype (SHW-L1) by crossing an Ae. tauschii ssp. tauschii accession (AS60) with a tetraploid wheat genotype (AS2255). A population consisting of 171 F8 recombinant inbred lines was developed from SHW-L1 and Chuanmai 32 to identify QTLs associated with agronomic traits. A new genetic map with high density was constructed and used to detect the QTLs for heading date, kernel width, spike length, spikelet number, and thousand kernel weight. A total of 30 putative QTLs were identified for five investigated traits. Thirteen QTLs were located on D genomes of SHW-L1, six of them showed positive effect on agronomic traits. Chromosome region flanked by wPt-6133–wPt-8134 on 2D carried five environment-independent QTLs. Each QTL accounted for more than 10% phenotypic variance. These QTLs were highly consistent across environments and should be used in wheat breeding.

Micronutrient malnutrition affects over three billion people worldwide, especially women and children in developing countries. Increasing the bioavailable concentrations of essential elements in the edible portions of crops is an effective resolution to address this issue. To determine the genetic factors controlling micronutrient concentration in wheat, the quantitative trait locus (QTL) analysis for iron, zinc, copper, manganese, and selenium concentrations in two recombinant inbred line populations was performed. In all, 39 QTLs for five micronutrient concentrations were identified in this study. Of these, 22 alleles from synthetic wheat SHW-L1 and seven alleles from the progeny line of the synthetic wheat Chuanmai 42 showed an increase in micronutrient concentrations. Five QTLs on chromosomes 2A, 3D, 4D, and 5B found in both the populations showed significant phenotypic variation for 2-3 micronutrient concentrations. Our results might help understand the genetic control of micronutrient concentration and allow the utilization of genetic resources of synthetic hexaploid wheat for improving micronutrient efficiency of cultivated wheat by using molecular marker-assisted selection.

Waterlogging is a widespread limiting factor for wheat production throughout the world. To identify quantitative trait loci (QTLs) associated with waterlogging tolerance at early stages of growth, survival rate (SR), germination rate index (GRI), leaf chlorophyll content index (CCI), root length index (RLI), plant height index (PHI), root dry weight index (RDWI), shoot dry weight index (SDWI), and total dry weight index (DWI) were assessed using the International Triticeae Mapping Initiative (ITMI) population W7984/Opata85. Significant and positive correlations were detected for all traits in this population except RLI. A total of 32 QTLs were associated with waterlogging tolerance on all chromosomes except 3A, 3D, 4B, 5A, 5D, 6A, and 6D. Some of the QTLs explained large proportions of the phenotypic variance. One of these is the QTL for GRI on 7A, which explained 23.92% of the phenotypic variation. Of them, 22 alleles from the synthetic hexaploid wheat W7984 contributed positively. These results suggested that synthetic hexaploid wheat W7984 is an important genetic resource for waterlogging tolerance in wheat. These alleles conferring waterlogging tolerance at early stages of growth in wheat could be utilized in wheat breeding for improving waterlogging tolerance.