A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data

<p>Abstract</p> <p>Background</p> <p>Identification of essential proteins is always a challenging task since it requires experimental approaches that are time-consuming and laborious. With the advances in high throughput technologies, a large number of protein-protein interactions are available, which have produced unprecedented opportunities for detecting proteins' essentialities from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to the robustness of network. Therefore, a new robust essential protein discovery method would be of great value.</p> <p>Results</p> <p>In this paper, we propose a new centrality measure, named PeC, based on the integration of protein-protein interaction and gene expression data. The performance of PeC is validated based on the protein-protein interaction network of <it>Saccharomyces cerevisiae</it>. The experimental results show that the predicted precision of PeC clearly exceeds that of the other fifteen previously proposed centrality measures: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Density of Maximum Neighborhood Component (DMNC), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC), Range-Limited Centrality (RL), L-index (LI), Leader Rank (LR), Normalized <it>α</it>-Centrality (NC), and Moduland-Centrality (MC). Especially, the improvement of PeC over the classic centrality measures (BC, CC, SC, EC, and BN) is more than 50% when predicting no more than 500 proteins.</p> <p>Conclusions</p> <p>We demonstrate that the integration of protein-protein interaction network and gene expression data can help improve the precision of predicting essential proteins. The new centrality measure, PeC, is an effective essential protein discovery method.</p

Effects of low-molecular-weight heparin and unfractionated heparin on traumatic disseminated intravascular coagulation

Purpose: To explore the effects of unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH) on traumatic disseminated  intravascular coagulation (DIC).Methods: A total of 77 cases of severe trauma (APACHE II score: 5 – 10) with DIC were collected and randomly assigned to one of three groups: LMWH treatment - 26 cases were subcutaneously injected with LMWH (75–150 units/kg/d); UFH treatment - 25 cases were subcutaneously  injected with UFH (100 – 250 units/kg/d); control - 26 cases supplemented with blood coagulation factor only. Daily mortality in the intensive care unit (ICU), hospitalization time, bleeding rate, thrombin time, prothrombin time, activated partial thromboplastin time, and levels of fibrinogen, antithrombin III (ATIII), and D-dimer were recorded and analyzed.Results: In ICU, LMWH and UFH treatments resulted in lower mortality than in the control group. In addition, hospitalization time was longer in patients treated with LMWH and UFH than in control patients. No significant  differences were found between LMWH-treated and control patients in terms of bleeding rate, but UFH-treated patients had lower bleeding rates than control patients. Multifactor analysis indicate a strong relationship between ATIII levels and bleeding rate.Conclusion: The results indicate that low-dose UFH and LMWH are effective options for the treatment of DIC.Keywords: Trauma, Disseminated intravascular coagulation, Unfractionated heparin, Low-molecularweight heparin, Fibrinogen, Antithrombi

Cavernous Transformation of the Portal Vein Might Increase the Risk of Liver Abscess

Cavernous transformation of the portal vein (CTPV) is not quite common in adults, and cases with CTPV and acute liver abscess are lacking. We report a patient with CTPV inducing extrahepatic and intrahepatic obstruction, finally leading to acute liver abscess due to bile duct infection. We aim to find out the possible relationship between CTPV and acute liver abscess. A 45-year-old female patient was admitted to our hospital for recurrent upper abdominal pain and distension for one year, aggravated with fever for three years. A diagnosis of CTPV and liver abscess was made by 16-slice computed tomography. Effective antibiotics and drainage were used for this patients, and she was eventually cured. When treating patients with CTPV, extrahepatic and intrahepatic obstruction, one should be aware of the presence of acute liver abscess, and empirical antibiotics might be valuable

Angiotensin-converting enzyme gene 2350 G/A polymorphism and susceptibility to atrial fibrillation in Han Chinese patients with essential hypertension

OBJECTIVE: The angiotensin-converting enzyme gene is one of the most studied candidate genes related to atrial fibrillation. Among the polymorphisms of the angiotensin-converting enzyme gene, the 2350 G/A polymorphism (rs4343) is known to have the most significant effects on the plasma angiotensin-converting enzyme concentration. The aim of the present study was to investigate the association of the angiotensin-converting enzyme 2350 G/A polymorphism with atrial fibrillation in Han Chinese patients with essential hypertension. METHODS: A total of 169 hypertensive patients were eligible for this study. Patients with atrial fibrillation (n = 75) were allocated to the atrial fibrillation group, and 94 subjects without atrial fibrillation were allocated to the control group. The PCR-based restriction fragment length polymorphism technique was used to assess the genotype frequencies. RESULTS: The distributions of the angiotensin-converting enzyme 2350 G/A genotypes (GG, GA, and AA, respectively) were 40.43%, 41.49%, and 18.08% in the controls and 18.67%, 46.67%, and 34.66% in the atrial fibrillation subjects (p = 0.037). The frequency of the A allele in the atrial fibrillation group was significantly greater than in the control group (58.00% vs. 38.83%, p = 0.0007). Compared with the wild-type GG genotype, the GA and AA genotypes had an increased risk for atrial fibrillation. Additionally, atrial fibrillation patients with the AA genotype had greater left atrial dimensions than the patients with the GG or GA genotypes (

UGC: Unified GAN Compression for Efficient Image-to-Image Translation

Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii)data/label-efficient learning with fewer training data/labels. To combine the best of both worlds, we propose a new learning paradigm, Unified GAN Compression (UGC), with a unified optimization objective to seamlessly prompt the synergy of model-efficient and label-efficient learning. UGC sets up semi-supervised-driven network architecture search and adaptive online semi-supervised distillation stages sequentially, which formulates a heterogeneous mutual learning scheme to obtain an architecture-flexible, label-efficient, and performance-excellent model

AlignDet: Aligning Pre-training and Fine-tuning in Object Detection

The paradigm of large-scale pre-training followed by downstream fine-tuning has been widely employed in various object detection algorithms. In this paper, we reveal discrepancies in data, model, and task between the pre-training and fine-tuning procedure in existing practices, which implicitly limit the detector's performance, generalization ability, and convergence speed. To this end, we propose AlignDet, a unified pre-training framework that can be adapted to various existing detectors to alleviate the discrepancies. AlignDet decouples the pre-training process into two stages, i.e., image-domain and box-domain pre-training. The image-domain pre-training optimizes the detection backbone to capture holistic visual abstraction, and box-domain pre-training learns instance-level semantics and task-aware concepts to initialize the parts out of the backbone. By incorporating the self-supervised pre-trained backbones, we can pre-train all modules for various detectors in an unsupervised paradigm. As depicted in Figure 1, extensive experiments demonstrate that AlignDet can achieve significant improvements across diverse protocols, such as detection algorithm, model backbone, data setting, and training schedule. For example, AlignDet improves FCOS by 5.3 mAP, RetinaNet by 2.1 mAP, Faster R-CNN by 3.3 mAP, and DETR by 2.3 mAP under fewer epochs.Comment: Accepted by ICCV 2023. Code and Models are publicly available. Project Page: https://liming-ai.github.io/AlignDe

AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration

Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undisputed principle of diffusion models. We consider that such a uniform assumption is not the optimal solution in practice; i.e., we can find different optimal time steps for different models. Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training. Specifically, we first design a unified search space that consists of all possible time steps and various architectures. Then, a two stage evolutionary algorithm is introduced to find the optimal solution in the designed search space. To further accelerate the search process, we employ FID score between generated and real samples to estimate the performance of the sampled examples. As a result, the proposed method is (i).training-free, obtaining the optimal time steps and model architecture without any training process; (ii). orthogonal to most advanced diffusion samplers and can be integrated to gain better sample quality. (iii). generalized, where the searched time steps and architectures can be directly applied on different diffusion models with the same guidance scale. Experimental results show that our method achieves excellent performance by using only a few time steps, e.g. 17.86 FID score on ImageNet 64 $\times$ 64 with only four steps, compared to 138.66 with DDIM. The code is available at https://github.com/lilijiangg/AutoDiffusion

Cognitive-enhancing effects of polygalasaponin hydrolysate in aβ(25-35)-induced amnesic mice.

Polygalasaponins are the major active constituents of Polygala tenuifolia exhibiting antiamnesic activity, but their applications are limited due to their toxicities. Evidence showed that the toxicities can be attenuated by hydrolysis. Herein, effects of a hydrolysate of polygalasaponins (HPS) on cognitive impairment induced by Aβ(25-35) were assessed by Morris water maze and step-through passive avoidance tests. The impaired spatial reference memory was improved by HPS (50 and 100 mg/kg). In the acquisition trial of step-through test, HPS (50 and 100 mg/kg) increased the latency into the dark chamber and decreased the error frequency significantly (P < .05). However, no significant change was observed during the retention trial. Additionally, HPS increased the corresponding SOD activities (62.34%, 22.09%) and decreased MDA levels (28.21%, 32.35%) in both cortex and hippocampus as compared to model animals. These results show that HPS may be a useful treatment against amnesia probably via its antioxidant properties.Peer Reviewe