In this paper, based on the multilingual morphological analyzer, we researched the similar low-resource languages, Uyghur and Kazakh, short text classification. Generally, the online linguistic resources of these languages are noisy. So a preprocessing is necessary and can significantly improve the accuracy. Uyghur and Kazakh are the languages with derivational morphology, in which words are coined by stems concatenated with suffixes. Usually, terms are used as the representation of text content while excluding functional parts as stop words in these languages. By extracting stems we can collect necessary terms and exclude stop words. Morpheme segmentation tool can split text into morphemes with 95% high reliability. After preparing both word- and morpheme-based training text corpora, we apply convolutional neural network (CNN) as a feature selection and text classification algorithm to perform text classification tasks. Experimental results show that the morpheme-based approach outperformed the word-based approach. Word embedding technique is frequently used in text representation both in the framework of neural networks and as a value expression, and can map language units into a sequential vector space based on context, and it is a natural way to extract and predict out-of-vocabulary (OOV) from context information. Multilingual morphological analysis has provided a convenient way for processing tasks of low resource languages like Uyghur and Kazakh.

Bulk skin transcriptomes cannot distinguish cell-intrinsic gene regulation from shifts in tissue-type composition, and computational deconvolution is widely applied to resolve this limitation. In this study, we performed deconvolution on published Hetian sheep skin RNA-seq data from three high-wool-density and three low-wool-density animals, using the available ovine skin single-cell reference. All hypotheses and analytical workflows were prospectively specified and timestamped prior to estimating cell-type fractions. Our work identifies two major methodological pitfalls for livestock tissue deconvolution. First, gene-length bias distorts deconvolution outputs derived from mixed bulk and 3′ single-cell references. After bias correction, the estimated hair-follicle epithelial fraction changed substantially. This bias cannot be uncovered by standard benchmark workflows, highlighting a hidden failure mode for deconvolution. Second, limited signature separability within the reference prevents reliable discrimination between related epidermal compartments. This non-identifiability strongly disturbs apparent compartment-level effects. When analysed at the granularity the reference reliably supports, we found no compositional difference between high- and low-wool-density groups for the epithelial hypothesis. The dermal hypothesis could not be meaningfully evaluated due to incompatible reference compartment merging requirements. Group-level differential expression remained robust despite this null compositional outcome. We recovered a large set of differentially expressed genes consistent with the original study, including most qPCR-validated candidates. Adjusting for cell-type composition altered part of this gene list, as expected given negligible group-level compositional divergence. Co-expression modules correlated more strongly with inter-individual compositional variation than with wool-density phenotype. This indicates compositional heterogeneity shapes transcriptome co-expression even without measurable mean differences across experimental groups. To avoid misleading biological inference, we propose a set of practical diagnostic checks for deconvolution when developmental or limited single-cell atlases are used for livestock tissues. Collectively, these transcriptomic patterns support cell-state-driven regulation rather than detectable mean shifts in tissue compartment composition.