A Fast Algorithm to Compute Maximum k-Plexes in Social Network Analysis

A clique model is one of the most important techniques on the cohesive subgraph detection; however, its applications are rather limited due to restrictive conditions of the model. Hence much research resorts to k-plex — a graph in which any vertex is adjacent to all but at most k vertices — which is a relaxation model of the clique. In this paper, we study the maximum k-plex problem and propose a fast algorithm to compute maximum k-plexes by exploiting structural properties of the problem. In an n-vertex graph, the algorithm computes optimal solutions in cnnO(1) time for a constant c < 2 depending only on k. To the best of our knowledge, this is the first algorithm that breaks the trivial theoretical bound of 2n for each k ≥ 3. We also provide experimental results over multiple real-world social network instances in support

Interactions of miR-34b/c

Several lines of evidence indicate that inflammatory processes play a key role in the happening and development of intracranial aneurysm (IA). Recently, polymorphisms in the TP53 gene were shown to be associated with inflammation and inflammatory disease. The aim of this study was to investigate the interactions of miR-34b/c and TP53 Arg72-Pro polymorphisms on the risk of IA in a Chinese population. A total of 590 individuals (including 164 patients with IA and 426 controls) were involved in this study. The polymorphisms (i.e., miR-34b/c rs4938723 and TP53 Arg72-Pro) were genotyped by polymerase chain reaction-restriction fragment length polymorphism assay and DNA sequencing. We found that the CC genotype of miR-34b/c rs4938723 was significantly associated with a decreased risk of IA compared with the TT genotype. Moreover, a significant gene interaction of the carriers with the combined genotypes of miR-34b/c rs4938723CC and TP53 Arg72Pro CG/CC/GG had a decreased risk of IA, compared with those carrying miR-34b/c rs4938723CT/TT+TP53 Arg72Pro GG/CG/CC combined genotypes. These findings suggest that the miR-34b/c rs4938723CC and TP53 Arg72-Pro polymorphisms may be involved in the susceptibility to IA

Scenarios of temporal environmental alterations and phytoplankton diversity in a changing bay in the East China Sea

In the context of global change, the stressors of warming and eutrophication have significant ecological implications in coastal waters. In order to examine the diversity of phytoplankton and its relationship with water quality, we conducted a survey of phytoplankton community compositions and their correlation with environmental changes over four seasons in a eutrophic bay located in the East China Sea. Through a systematic analysis, we identified diatoms and dinoflagellates as the primary dominant groups, with the species Skeletonema costatum, Skeletonema marinoi, Biddulphia sinensis, Thalassiosira eccentrica, Leptocylindrus danicus, Coscinodiscus oculus-iridis, Coscinodiscus jonesianus, and Chaetoceros knipowitschi as the most abundant species in all seasons. Significant seasonal alterations were observed in both environmental settings and phytoplankton species richness, dominance, and abundance. The phytoplankton community varied in its response to diverse aquatic environments and was principally affected by temperature, silicic acid concentrations, and suspended solids. Elevated temperatures were found to promote an increase in phytoplankton abundance. However, no clear evidence of diatom and dinoflagellate succession in relation to N:P ratio was observed across seasons. Water quality analysis illustrated that the majority of the study area exhibited a mid-eutrophic with severe organic pollution. The abundance of phytoplankton was significantly influenced by eutrophication and organic pollution. The accelerated warming process related to coastal nuclear power plants and nutrient regime alterations significantly affect the temporal shift of the phytoplankton community. These findings contribute valuable insights into the effects of eutrophic environments on the structure of phytoplankton communities in coastal aquatic systems

The Diversity of Scuticociliates (Protozoa, Ciliophora): A Report on Eight Marine Forms Found in Coastal Waters of China, with a Description of One New Species

Eight marine scuticociliates, Pseudoplatynematum denticulatum (Kahl, 1933) nov. comb., Protocyclidium sinica nov. spec., Histiobalantium marinum Kahl, 1933, Porpostoma notata Möbius, 1888, Philaster hiatti Thompson, 1969, Parauronema longum Song, 1995, Uronemella parafilificum Gong et al., 2007, and Paranophrys magna Borror, 1972, collected from Chinese coastal waters, were investigated using live observations and silver impregnation methods. Investigations of a Chinese population of Platynematum denticulatum (Kahl, 1933) reveal that it has a highly strengthened pellicle and distinct spines and thus corresponds well with the definition of Pseudoplatynematum Bock, 1952. A new combination, Pseudoplatynematum denticulatum (Kahl, 1933) nov. comb., is therefore proposed and an improved species diagnosis is supplied. Protocyclidium sinica nov. spec. is characterized by: small body size with buccal field approximately 60% of body length; extrusomes present; 13 or 14 somatic kineties; somatic kinety 1 comprising approximately 24 densely arranged kinetids; somatic kinety n shortened posteriorly; single macronucleus. Additional information is documented on the morphology of six other species of scuticociliates based on the China populations

A comparative analysis of sleep spindle characteristics of sleep-disordered patients and normal subjects

Spindles differ in density, amplitude, and frequency, and these variations reflect different physiological processes. Sleep disorders are characterized by difficulty in falling asleep and maintaining sleep. In this study, we proposed a new spindle wave detection algorithm, which was more effective compared with traditional detection algorithms such as wavelet algorithm. Besides, we recorded EEG data from 20 subjects with sleep disorders and 10 normal subjects, and then we compared the spindle characteristics of sleep-disordered subjects and normal subjects (those without any sleep disorder) to assess the spindle activity during human sleep. Specifically, we scored 30 subjects on the Pittsburgh Sleep Quality Index and then analyzed the association between their sleep quality scores and spindle characteristics, reflecting the effect of sleep disorders on spindle characteristics. We found a significant correlation between the sleep quality score and spindle density (p = 1.84 × 10−8, p-value <0.05 was considered statistically significant.). We, therefore, concluded that the higher the spindle density, the better the sleep quality. The correlation analysis between the sleep quality score and mean frequency of spindles yielded a p-value of 0.667, suggesting that the spindle frequency and sleep quality score were not significantly correlated. The p-value between the sleep quality score and spindle amplitude was 1.33 × 10−4, indicating that the mean amplitude of the spindle decreases as the score increases, and the mean spindle amplitude is generally slightly higher in the normal population than in the sleep-disordered population. The normal and sleep-disordered groups did not show obvious differences in the number of spindles between symmetric channels C3/C4 and F3/F4. The difference in the density and amplitude of the spindles proposed in this paper can be a reference characteristic for the diagnosis of sleep disorders and provide valuable objective evidence for clinical diagnosis. In summary, our proposed detection method can effectively improve the accuracy of sleep spindle wave detection with stable performance. Meanwhile, our study shows that the spindle density, frequency and amplitude are different between the sleep-disordered and normal populations

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl