Genetic Analysis of Cotton Fiber Traits in Gossypium Hybrid Lines.

Cotton plays a crucial role in the progress of the textile industry and the betterment of human life by providing natural fibers. In our study, we explored the genetic determinants of cotton architecture and fiber yield and quality by crossbreeding Gossypium hirsutum and Gossypium barbadense, creating a recombinant inbred line (RIL) population. Utilizing SNP markers, we constructed an extensive genetic map encompassing 7,730 markers over 2,784.2 cM. We appraised two architectural and seven fiber traits within six environments, identifying 58 QTLs, of which 49 demonstrated stability across these environments. These encompassed QTLs for traits such as lint percentage (LP), boll weight (BW), fiber strength (STRENGTH), seed index (SI), and micronaire (MIC), primarily located on chromosomes chr-A07, chr-D06, and chr-D07. Notably, chr-D07 houses a QTL region affecting SI, corroborated by multiple studies. Within this region, the genes BZIP043 and SEP2 were identified as pivotal, with SEP2 particularly showing augmented expression in developing ovules. These discoveries contribute significantly to marker-assisted selection, potentially elevating both the yield and quality of cotton fiber production. These findings provide valuable insights into marker-assisted breeding strategies, offering crucial information to enhance fiber yield and quality in cotton production.

Decoding the guardians of cotton resilience: A comprehensive exploration of the ßCA genes and its role in Verticillium dahliae resistance.

Plant Carbonic anhydrases (Cas) have been shown to be stress-responsive enzymes that may play a role in adapting to adverse conditions. Cotton is a significant economic crop in China, with upland cotton (Gossypium hirsutum) being the most widely cultivated species. We conducted genome-wide identification of the ßCA gene in six cotton species and preliminary analysis of the ßCA gene in upland cotton. In total, 73 ßCA genes from six cotton species were identified, with phylogenetic analysis dividing them into five subgroups. GHßCA proteins were predominantly localized in the chloroplast and cytoplasm. The genes exhibited conserved motifs, with motifs 1, 2, and 3 being prominent. GHßCA genes were unevenly distributed across chromosomes and were associated with stress-responsive cis-regulatory elements, including those responding to light, MeJA, salicylic acid, abscisic acid, cell cycle regulation, and defence/stress. Expression analysis indicated that GHßCA6, GHßCA7, GHßCA10, GHßCA15, and GHßCA16 were highly expressed under various abiotic stress conditions, whereas GHßCA3, GHßCA9, GHßCA10, and GHßCA18 had higher expression patterns under Verticillium dahliae infection at different time intervals. In Gossypium thurberi, GthßCA1, GthßCA2, and GthßCA4 showed elevated expression across stress conditions and tissues. Silencing GHßCA10 through VIGS increased Verticillium wilt severity and reduced lignin deposition compared to non-silenced plants. GHßCA10 is crucial for cotton's defense against Verticillium dahliae. Further research is needed to understand the underlying mechanisms and develop strategies to enhance resistance against Verticillium wilt.

CORONet: A Cross-Sequence Joint Representation and Hypergraph Convolutional Network for Classifying Molecular Subtypes of Breast Cancer Using Incomplete DCE-MRI.

Breast cancer, the predominant malignancy among women, is characterized by significant heterogeneity, leading to the emergence of distinct molecular subtypes. Accurate differentiation of these molecular subtypes holds paramount clinical significance, owing to substantial variations in prognosis, therapeutic strategies, and survival outcomes. In this study, we propose a cross-sequence joint representation and hypergraph convolution network (CORONet) for classifying molecular subtypes of breast cancer using incomplete DCE-MRI. Specifically, we first build a cross-sequence joint representation (COR) module to integrate image imputation and feature representation into a unified framework, encouraging effective feature extraction for subsequent classification. Then, we fuse multiple COR features and applied feature selection to reduce the redundant information between sequences. Finally, we deploy hypergraph structures to model high-order correlation among different subjects and extracted high-level semantic features by hypergraph convolutions for molecular subtyping. Extensive experiments on incomplete DCE-MRIs of 395 patients from the TCIA repository showed a significant improvement of our CORONet over state of the arts, with the area under the curve (AUC) of 0.891 and 0.903 for luminal and triple-negative (TN) subtype prediction, respectively. Similar advantages of CORONet were also confirmed in partial complete DCE-MRIs of 144 patients, achieving an AUC of 0.858 and 0.832 for predicting luminal and TN subtypes of breast cancer, respectively. Nevertheless, both of these values were lower compared to the scenario where DCE-MRIs from all 395 patients were utilized. Our study contributes to the precise molecular subtyping using incomplete multi-sequence DCE-MRI, thereby offering promising prospects for future risk stratification of breast cancer patients.

[Research Process on the Combined Pollution of Microplastics and Typical Pollutants in Agricultural Soils].

As a new type of environmental persistent pollutant， microplastics can not only have adverse effects on the ecosystem but also form complex pollution with co-existing pollutants in the surrounding environment， resulting in higher ecological and health risks. Based on the perspective of agroecosystems， this study focused on the combined pollution of heavy metals， pesticides， and antibiotics， which are three typical pollutants of farmland soil， as well as microplastics and discussed the adsorption-desorption behavior of heavy metals， pesticides， and antibiotics on microplastics. The influence of the structure and properties of microplastics， the physicochemical properties of pollutants， and environmental conditions on the adsorption and desorption behavior of heavy metals， pesticides， and antibiotics on microplastics was discussed. The influence of microplastics on the bioavailability of heavy metals， pesticides， and antibiotics in farmland soil and the internal mechanism were expounded. The existing problems and shortcomings of current research were pointed out， and the future research direction was proposed. This study can provide a scientific reference for ecological risk assessment of the combined pollution of microplastics and typical pollutants in farmland soil.

A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.

RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes. MATERIALS AND METHODS: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues. Second, we developed a multi-branch convolutional-neural-network (MBCNN) to perform molecular subtype prediction. Third, we assessed the MBCNN with different regions of interest (ROIs) and fusion strategies, and compared it to previous DL models. Area under the curve (AUC) and accuracy (ACC) were used to assess different models. Delong-test was used for the comparison of different groups. RESULTS: MBCNN achieved the optimal performance under intermediate fusion and ROI size of 80 pixels with appearance transformation. It outperformed CNN and convolutional long-short-term-memory (CLSTM) in predicting luminal B, HER2-enriched and TN subtypes, but without demonstrating statistical significance except against CNN in TN subtypes, with testing AUCs of 0.8182 vs. [0.7208, 0.7922] (p=0.44, 0.80), 0.8500 vs. [0.7300, 0.8200] (p=0.36, 0.70) and 0.8900 vs. [0.7600, 0.8300] (p=0.03, 0.63), respectively. When predicting luminal A, MBCNN outperformed CNN with AUCs of 0.8571 vs. 0.7619 (p=0.08) without achieving statistical significance, and is comparable to CLSTM. For four-subtype prediction, MBCNN achieved an ACC of 0.64, better than CNN and CLSTM models with ACCs of 0.48 and 0.52, respectively. CONCLUSION: Developed DL model with the feature extraction and fusion of DCE-MRI from two institutes enabled preoperative prediction of breast cancer molecular subtypes with high diagnostic performance.

Distribution and bulk flow analyses of the intraflagellar transport (IFT) motor kinesin-2 support an "on-demand" model for Chlamydomonas ciliary length control.

Most cells tightly control the length of their cilia. The regulation likely involves intraflagellar transport (IFT), a bidirectional motility of multi-subunit particles organized into trains that deliver building blocks into the organelle. In Chlamydomonas, the anterograde IFT motor kinesin-2 consists of the motor subunits FLA8 and FLA10 and the nonmotor subunit KAP. KAP dissociates from IFT at the ciliary tip and diffuses back to the cell body. This observation led to the diffusion-as-a-ruler model of ciliary length control, which postulates that KAP is progressively sequestered into elongating cilia because its return to the cell body will require increasingly more time, limiting motor availability at the ciliary base, train assembly, building block supply, and ciliary growth. Here, we show that Chlamydomonas FLA8 also returns to the cell body by diffusion. However, more than 95% of KAP and FLA8 are present in the cell body and, at a given time, just ~1% of the motor participates in IFT. After repeated photobleaching of both cilia, IFT of fluorescent kinesin subunits continued indicating that kinesin-2 cycles from the large cell-body pool through the cilia and back. Furthermore, growing and full-length cilia contained similar amounts of kinesin-2 subunits and the size of the motor pool at the base changed only slightly with ciliary length. These observations are incompatible with the diffusion-as-a-ruler model, but rather support an "on-demand model," in which the cargo load of the trains is regulated to assemble cilia of the desired length.

Overexpression and knockdown of cotton GhdadD gene reveals its drought and salt stress tolerance role.

The 5'-deoxyadenosine deaminase (DADD), a member of the amidohydrolase family regulates biological purine metabolism. In this study, bioinformatic analysis, overexpression and knockdown of GhdadD gene were detected to identify its potential role in drought and salt stress tolerance. The results revealed that GhdadD was induced by ABA, Auxin, MBS and light responsive elements. In transgenic Arabidopsis, seed germination rate and root length were increased under drought or salt stress. GhdadD overexpressed seedlings resulted in higher plant height, less leaf damage and lower ion permeability. The expression of osmotic stress and ABA-responsive genes were up regulated. While in GhdadD-silenced cotton seedlings, CAT, SOD activity and soluble sugar content were reduced, MDA content was increased, and the stoma opening was depressed under drought or salt stress. Some osmics stress marker genes were also up regulated. These data indicating that GhdadD enhanced plant resistance to drought and salt stress through ABA pathways.

A hybrid spatiotemporal deep belief network and sparse representation-based framework reveals multilevel core functional components in decoding multitask fMRI signals.

Decoding human brain activity on various task-based functional brain imaging data is of great significance for uncovering the functioning mechanism of the human mind. Currently, most feature extraction model-based methods for brain state decoding are shallow machine learning models, which may struggle to capture complex and precise spatiotemporal patterns of brain activity from the highly noisy fMRI raw data. Moreover, although decoding models based on deep learning methods benefit from their multilayer structure that could extract spatiotemporal features at multiscale, the relatively large populations of fMRI datasets are indispensable, and the explainability of their results is elusive. To address the above problems, we proposed a computational framework based on hybrid spatiotemporal deep belief network and sparse representations to differentiate multitask fMRI (tfMRI) signals. Using a relatively small cohort of tfMRI data as a test bed, our framework can achieve an average classification accuracy of 97.86% and define the multilevel temporal and spatial patterns of multiple cognitive tasks. Intriguingly, our model can characterize the key components for differentiating the multitask fMRI signals. Overall, the proposed framework can identify the interpretable and discriminative fMRI composition patterns at multiple scales, offering an effective methodology for basic neuroscience and clinical research with relatively small cohorts.