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The genetic polymorphisms of 15 autosomal short tandem repeat (STR) loci were analyzed in 449 individuals of the Uygur population from Ili Kazakh Autonomous Prefecture, Northwestern China. Phylogenetic analysis was performed among the Ili Uygur population and other relevant populations. The neighbor‐joining tree and multidimensional scaling plot were generated based on the Nei's standard genetic distance. We found a total of 173 alleles with corresponding frequencies ranging from 0.5022 to 0.0011. The combined powers of discrimination and exclusion for the 15 autosomal STR loci were 0.99999999985 and 0.99999880065, respectively. Population comparisons indicated that the Ili Uygur population had a relatively close genetic relationship with the Uygur populations from other regions of China. The pairwise genetic distance and P‐values between Ili Uygur and 10 published populations showed that no statistically significant differences existed between the Ili Uygur population and the Kashi, Kashgar, and Kotan Uygur. Therefore, the Ili Uygur population has its own unique Uygur genetic characteristics that were different from the other ethnic populations of China. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L1/2 constraint (L1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others. [ABSTRACT FROM AUTHOR]

[Objective] The techniques of commercialized storage and fresh-keeping by avoiding sweat for post-harvest navel orange were studied to provide technical support for the healthy development of navel orange industry. [Method]On the basis of previous independent research and development of sweat-avoiding pretreatment techniques with medium temperature for post-harvest navel orange, timely harvested navel oranges were used as raw materials. Single-factor and orthogonal experiments were conducted to investigate the effects of different compound ratios of antistaling agent, waxing conditions, packaging materials and storage methods on preservation of navel orange, following which the optimized procedure and technique of commercialized processing, storage and preservation for post-harvest navel orange were obtained. [Result]Compound ratios of antistaling agent, waxing conditions, packaging materials and storage methods had great influence on preserving effects of navel orange. The optimized procedure and technique of commercialized storage and preservation for post-harvest navel orange was as follows. Clean and disinfect post-harvest navel oranges, soaking at a constant temperature of 35 °C in antisepsis and fresh-keeping liquid with 40% Bellkute (ratio of 1000 times) and 22.2% Imazalil (500 times), and were then treated by fruit wax (concentration of 1"4 and temperature of 40°C). After air-drying, single fruit was put into film bags 30 s after the wax was dry and the fruits were packed with miscellaneous wooden boxes, and at last, they were placed in a negative-pressure storeroom with strong-exhausting ventilation exhaust and controlled temperature for storage. Under the above preservation condition, orange fruits were preserved for 150 days, with a good fruit rate of 95.40%, and fruit quality remained good. [Conclusion] Utilizing techniques of commercialized storage and fresh-keeping by avoiding sweat for post-harvest navel orange (soaking fruits in antisepsis and fresh-keeping liquid+spraying them with wax+packaging single fruit with film bag +putting them into miscellaneous wooden boxes+preserving them in a negative-pressure storeroom with strong-exhausting ventilation exhaust and controlled temperature) can improve good fruit rate and keep good fruit quality for post-harvest navel orange. [ABSTRACT FROM AUTHOR]

Federated cross learning has shown impressive performance in medical image segmentation. However, it encounters the catastrophic forgetting issue caused by data heterogeneity across different clients and is particularly pronounced when simultaneously facing pixelwise label deficiency problem. In this article, we propose a novel federated cross-incremental self-supervised learning method, coined FedCSL, which not only can enable any client in the federation incrementally yet effectively learn from others without inducing knowledge forgetting or requiring massive labeled samples, but also preserve maximum data privacy. Specifically, to overcome the catastrophic forgetting issue, a novel cross-incremental collaborative distillation (CCD) mechanism is proposed, which distills explicit knowledge learned from previous clients to subsequent clients based on secure multiparty computation (MPC). Besides, an effective retrospect mechanism is designed to rearrange the training sequence of clients per round, further releasing the power of CCD by enforcing interclient knowledge propagation. In addition, to alleviate the need of large-scale densely annotated pretraining medical datasets, we also propose a two-stage training framework, in which federated cross-incremental self-supervised pretraining paradigm first extracts robust yet general image-level patterns across multi-institutional data silos via a novel round-robin distributed masked image modeling (MIM) pipeline; then, the resulting visual concepts, e.g., semantics, are transferred to the federated cross-incremental supervised fine-tuning paradigm, favoring various cross-silo medical image segmentation tasks. The experimental results on public datasets demonstrate the effectiveness of the proposed method as well as the consistently superior performance of our method over most state-of-the-art methods quantitatively and qualitatively.

Human beings can leverage knowledge from relative tasks to improve learning on a primary task. Similarly, multitask learning (MTL) methods suggest using auxiliary tasks to enhance a neural network's performance on a specific primary task. However, previous methods often select auxiliary tasks carefully but treat them as secondary during training. The weights assigned to auxiliary losses are typically smaller than the primary loss weight, leading to insufficient training on auxiliary tasks and ultimately failing to support the main task effectively. To address this issue, we propose an uncertainty-based impartial learning method that ensures balanced training across all tasks. In addition, we consider both gradients and uncertainty information during backpropagation to further improve performance on the primary task. Extensive experiments show that our method achieves performance comparable to or better than state-of-the-art approaches. Moreover, our weighting strategy is effective and robust in enhancing the performance of the primary task regardless of the noise auxiliary tasks' pseudolabels.

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this article, we propose the dual-octave network (DONet), which is capable of learning multiscale spatial-frequency features from both the real and imaginary components of MR data, for parallel fast MR image reconstruction. More specifically, our DONet consists of a series of dual-octave convolutions (Dual-OctConvs), which are connected in a dense manner for better reuse of features. In each Dual-OctConv, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary) and then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intragroup information updating and intergroup information exchange to aggregate the contextual information across different groups. Our framework provides three appealing benefits: 1) it encourages information interaction and fusion between the real and imaginary components at various spatial frequencies to achieve richer representational capacity; 2) the dense connections between the real and imaginary groups in each Dual-OctConv make the propagation of features more efficient by feature reuse; and 3) DONet enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. Extensive experiments on two popular datasets (i.e., clinical knee and fastMRI), under different undersampling patterns and acceleration factors, demonstrate the superiority of our model in accelerated parallel MR image reconstruction.

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adopting L 1/2 constraint ( L 1/2 gLPCA) on error function for feature (gene) extraction. The error function based on L 1/2 -norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.