Phylogeny of the genus Morus (Urticales: Moraceae) inferred from ITS and trnL-F sequences

Both nuclear ribosomal ITS and chloroplast trnL-F sequences were acquired from 13 mulberry genotypes belonging to nine species and three varieties, and one paper mulberry. The later belongs to genus B. papyrifera, designed as outgroup, and were analyzed. Within the genus Morus, the sequence diversity of ITS was much higher than that of trnL-F. The results of phylogenetic analyses based on these data (separately or combined) show that the genus Morus is monophyletic group. Strict consensus tree obtained through the Neighbor-joining method can be divided into five major clades in the genus Morus, according to combined sequence data. M. bombycis, M. alba var. venose formed clades A and B, respectively. Clade C comprises of 5 species; M. rotundiloba, M. atropurpurea, M. mongolica, M. australi, and M. mongolica var. diabolica. Clade D comprises of 3 species; M. wittiorum, M. laevigata, and M. alba. Clade E comprises of 2 species; M. multicaulis, and M.alba var. macrophylla. The results from cluster analysis were basically in agreement with the existing morphologic classification.African Journal of Biotechnology Vol. 4 (6), pp. 563-569, 200

Improved Wavelet Threshold for Image De-noising

With the development of communication technology and network technology, as well as the rising popularity of digital electronic products, an image has become an important carrier of access to outside information. However, images are vulnerable to noise interference during collection, transmission and storage, thereby decreasing image quality. Therefore, image noise reduction processing is necessary to obtain higher-quality images. For the characteristics of its multi-analysis, relativity removal, low entropy, and flexible bases, the wavelet transform has become a powerful tool in the field of image de-noising. The wavelet transform in application mathematics has a rapid development. De-noising methods based on wavelet transform is proposed and achieved with good results, but shortcomings still remain. Traditional threshold functions have some deficiencies in image de-noising. A hard threshold function is discontinuous, whereas a soft threshold function causes constant deviation. To address these shortcomings, a method for removing image noise is proposed in this paper. First, the method decomposes the noise image to determine the wavelet coefficients. Second, the wavelet coefficient is applied on the high-frequency part of the threshold processing by using the improved threshold function. Finally, the de-noised images are obtained to rebuild the images in accordance with the estimation in the wavelet-based conditions. Experiment results show that this method, discussed in this paper, is better than traditional hard threshold de-noising and soft threshold de-noising methods, in terms of objective effects and subjective visual effects

Enhanced Gaussian Bare-Bones Grasshopper Optimization: Mitigating the Performance Concerns for Feature Selection

As a recent meta-heuristic algorithm, the uniqueness of the grasshopper optimization algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective optimization cases. Despite its advanced optimization ability, the basic GOA has a set of shortcomings that pose challenges in numerous practical scenarios. The GOA core limit is its early convergence to the local optimum and suffering from slow convergence. To mitigate these concerns, this study adopts the elite opposition-based learning and bare-bones Gaussian strategy to extend GOA\u27s global and local search capabilities and effectively balance the exploration and exploitation inclinations. Specifically, elite opposition-based learning can help find better solutions at the early stage of exploration, while the bare-bones Gaussian strategy has an excellent ability to update the search agents. To evaluate the robustness of the proposed Enhanced GOA (EGOA) based on global constrained and unconstrained optimization problems, a straight comparison was made between the proposed EGOA and other meta-heuristics on 30 IEEE CEC2017 benchmark tasks. Moreover, we applied it experimentally to structural design problems and its binary version to the feature selection cases. Findings demonstrate the effectiveness of EGOA and its binary version as an acceptable tool for optimization and feature selection purposes

MTANS:Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images

BackgroundGastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis.MethodsIn this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score.ResultsThe segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set.ConclusionThe results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications

Achievable rates for full-duplex massive MIMO systems with low-resolution ADCs/DACs under imperfect CSI environment

We investigate the uplink and downlink achievable rates of full-duplex (FD) massive multi-input multi-output (MIMO) systems with low-resolution analog-digital converters/digital-to-analog converters (ADCs/DACs), where maximum ratio combining/maximum ratio transmission (MRC/MRT) processing are adopted and imperfect channel state information (CSI) is assumed. In this paper, the quantization noise is encapsulated as an additive quantization noise model (AQNM). Then, employing the minimum mean-square error (MMSE) channel estimator, approximate expressions of the uplink and downlink achievable rates are derived, based on the analysis of the quantization error, loop interference (LI), and the inter-user interference (IUI). It is shown that the interference and noise can be eliminated by applying power scaling law properly and increasing the number of antennas. Moreover, given the number of antennas, it is found that the uplink and downlink approximate achievable rates will converge to a constant when the number of quantization bit tends to infinity. Therefore, the system performance that can be improved by increasing ADC/DAC resolution is limited, implying that it is reasonable to adopt low-resolution ADCs/DACs in FD massive MIMO systems

Safety and Immunogenicity of a Malaria Vaccine, Plasmodium falciparum AMA-1/MSP-1 Chimeric Protein Formulated in Montanide ISA 720 in Healthy Adults

The P. falciparum chimeric protein 2.9 (PfCP-2.9) consisting of the sequences of MSP1-19 and AMA-1 (III) is a malaria vaccine candidate that was found to induce inhibitory antibodies in rabbits and monkeys. This was a phase I randomized, single-blind, placebo-controlled, dose-escalation study to evaluate the safety and immunogenicity of the PfCP-2.9 formulated with a novel adjuvant Montanide ISA720. Fifty-two subjects were randomly assigned to 4 dose groups of 10 participants, each receiving the test vaccine of 20, 50, 100, or 200 µg respectively, and 1 placebo group of 12 participants receiving the adjuvant only.The vaccine formulation was shown to be safe and well-tolerated, and none of the participants withdrew. The total incidence of local adverse events (AEs) was 75%, distributed among 58% of the placebo group and 80% of those vaccinated. Among the vaccinated, 65% had events that were mild and 15% experienced moderate AEs. Almost all systemic adverse reactions observed in this study were graded as mild and required no therapy. The participants receiving the test vaccine developed detectable antibody responses which were boosted by the repeated vaccinations. Sixty percent of the vaccinated participants had high ELISA titers (>1∶10,000) of antigen-specific antibodies which could also recognize native parasite proteins in an immunofluorescence assay (IFA).This study is the first clinical trial for this candidate and builds on previous investigations supporting PfCP-2.9/ISA720 as a promising blood-stage malaria vaccine. Results demonstrate safety, tolerability (particularly at the lower doses tested) and immunogenicity of the formulation. Further clinical development is ongoing to explore optimizing the dose and schedule of the formulation to decrease reactogenicity without compromising immunogenicity.