Superhumps in a Peculiar SU UMa-Type Dwarf Nova ER Ursae Majoris

We report the photometry of a peculiar SU UMa-type dwarf nova - ER UMa for ten nights during 1998 December and 1999 March covering a complete rise to the supermaximum and a normal outburst cycle. Superhumps have been found during the rise to the superoutburst. A negative superhump appeared in Dec.22 light curve, while the superhump on the next night became positive and had large amplitude and distinct waveform from that of the previous night. In the normal outburst we captured, superhumps with larger or smaller amplitudes seem to always exist, although it is not necessarily true for every normal outburst. These results show great resemblance with V1159 Ori (Patterson et al. 1995). It is more likely that superhumps occasionally exist at essentially all phases of the eruption cycles of ER UMa stars, which should be considered in modeling.Comment: 4 pages, 5 figures, Accepted by ApJ Letter

Galaxy Morphology Classification Using Multi-Scale Convolution Capsule Network

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multi-scale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers through using a multi-branch structure to extract multi-scale hidden features of galaxy images. In order to further explore the hidden information in the features, the multi-scale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieving 97% accuracy, 96% precision, 98% recall, and 97% F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation were accomplished in this study. We visualized the morphological features for the part of sample set, which using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The results shows that the model has the better generalization ability and robustness, it can be effectively used in the galaxy morphological classification

The Study of Randomized Visual Saliency Detection Algorithm

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results

Utilization of Polislidae Wasp Venom as Potential New Insect Drugs in the R&D of Wellness Industry

The Polislidae wasp, one species of omnivorous social insects mainly living in the bush or under the leaves. The wasp has a venom sac in its tail, and the venom secreted by a sting can cause a series of body reactions and diseases. Multiple organ failure could be the outcome of wasp sting, if timely treatment or rescue has not been performed. Based on published reports on wasp sting related to medical concerns in recent years, this review summarizes the symptoms caused by wasp sting and corresponding mechanisms of actions. The medical application and relational utilization of the title insect is suggested derived from findings of the systematic review. Furthermore, we herewith sketch the perspectives of R&D on the venom of Polislidae wasp. It is expected to afford comprehensive references and be useful for broader study on the natural components and pharmacological effects of wasp venom

Recent Advances in L-Methionine Biosynthesis in Metabolically Engineered Corynebacterium glutamicum and Escherichia coli

L-Methionine is the only sulfur-containing essential amino acid. It acts as a precursor in the synthesis of various biologically active substances and participates in various metabolic pathways in the body. It is widely used in food, animal feed, medicine, cosmetics, and other fields. In recent years, since L-methionine is the only essential amino acid that cannot be industrially produced by microbial fermentation, the potential of metabolic engineering to improve L-methionine production has received widespread attention from researchers around the world. In this paper, the biosynthesis pathways and metabolic regulation mechanisms of L-methionine in Corynebacterium glutamicum and Escherichia coli are analyzed and compared. The metabolic engineering strategies to produce L-methionine are reviewed from five aspects: the removal of feedback inhibition of key enzymes, the cut-off or weakening of branch metabolic pathways, the optimization of the central metabolic regulatory network, the enhancement of cofactor supply, and the optimization of transport systems, and recent progress in research on the biosynthesis of L-methionine is summarized. Finally, future prospects are also discussed. It is hoped that this review will provide a basis for the breeding of high-yield L-methionine-producing strains

The effect of concentration and duration of normobaric oxygen in reducing caspase-3 and -9 expression in a rat-model of focal cerebral ischaemia

The aim of this study was to determine the effect of different concentrations of normobaric oxygen (NBO) on neurological function and the expression of caspase-3 and -9 in a rat model of acute cerebral ischaemia. Sprague-Dawley rats (n=120) were randomly divided into four groups (n=30 per group), including 3 groups given NBO at concentrations of 33%, 45% or 61% and one control group given air (21% oxygen). After 2 h of ischaemic occlusion, each group was further subdivided into six subgroups (n=5) during reperfusion according to the duration (3, 6, 12, 24, 48 or 72 h) and concentration of NBO (33%, 45% or 61%) or air treatment. The Fluorescence Quantitative polymerase chain reaction (PCR) and immunohistochemistry were used to detect caspase-3 and -9 mRNA and protein relative expression respectively. The Neurologic Impairment Score (NIS) was significantly lower in rats given 61% NBO ≥3 h after reperfusion when compared to the control group (P<0.05, Mann–Whitney U). NBO significantly reduced caspase-3 and -9 mRNA and protein expression when compared to the control group at all NBO concentrations and time points (P<0.05, ANOVA). The expression of caspase-3 and -9 was lower in the group given 61% NBO compared any other group, and this difference was statistically significant when compared to the group given 33% NBO for ≥48 h and the control group (both P<0.05, ANOVA). These findings indicate that NBO may inhibit the apoptotic pathway by reducing caspase-3 and -9 expression, thereby promoting neurological functional recovery after stroke

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