1,117 research outputs found
Efficient Truss Maintenance in Evolving Networks
Truss was proposed to study social network data represented by graphs. A
k-truss of a graph is a cohesive subgraph, in which each edge is contained in
at least k-2 triangles within the subgraph. While truss has been demonstrated
as superior to model the close relationship in social networks and efficient
algorithms for finding trusses have been extensively studied, very little
attention has been paid to truss maintenance. However, most social networks are
evolving networks. It may be infeasible to recompute trusses from scratch from
time to time in order to find the up-to-date -trusses in the evolving
networks. In this paper, we discuss how to maintain trusses in a graph with
dynamic updates. We first discuss a set of properties on maintaining trusses,
then propose algorithms on maintaining trusses on edge deletions and
insertions, finally, we discuss truss index maintenance. We test the proposed
techniques on real datasets. The experiment results show the promise of our
work
PUEPro : A Computational Pipeline for Prediction of Urine Excretory Proteins
This work is supported by the National Natural Science Foundation of China (Grant Nos. 81320108025, 61402194, 61572227), Development Project of Jilin Province of China (20140101180JC) and China Postdoctoral Science Foundation (2014T70291).Postprin
10-Benzyl-10H-phenothiazine 9-oxide. Corrigendum
Corrigendum to Acta Cryst. (2009), E65, o1799
3-Butyl-2-phenyl-1,3-thiazolidine-1,4-dione
In the title compound, C13H17NO2S, the thiazolidine-1,4-dione ring adopts an envelope conformation with the S atom lying 0.631 (4) Å out of the plane formed by the other four ring atoms; the phenyl ring is almost perpendicular [88.74 (8)°] with respect to the ring C—C—N—C atoms and the butyl chain is in a fully extended conformation. In the crystal, a supramolecular two-dimensional arrangement arises from weak intermolecular C—H⋯O interactions
CLIP Brings Better Features to Visual Aesthetics Learners
The success of pre-training approaches on a variety of downstream tasks has
revitalized the field of computer vision. Image aesthetics assessment (IAA) is
one of the ideal application scenarios for such methods due to subjective and
expensive labeling procedure. In this work, an unified and flexible two-phase
\textbf{C}LIP-based \textbf{S}emi-supervised \textbf{K}nowledge
\textbf{D}istillation paradigm is proposed, namely \textbf{\textit{CSKD}}.
Specifically, we first integrate and leverage a multi-source unlabeled dataset
to align rich features between a given visual encoder and an off-the-shelf CLIP
image encoder via feature alignment loss. Notably, the given visual encoder is
not limited by size or structure and, once well-trained, it can seamlessly
serve as a better visual aesthetic learner for both student and teacher. In the
second phase, the unlabeled data is also utilized in semi-supervised IAA
learning to further boost student model performance when applied in
latency-sensitive production scenarios. By analyzing the attention distance and
entropy before and after feature alignment, we notice an alleviation of feature
collapse issue, which in turn showcase the necessity of feature alignment
instead of training directly based on CLIP image encoder. Extensive experiments
indicate the superiority of CSKD, which achieves state-of-the-art performance
on multiple widely used IAA benchmarks
Warburg Effects in Cancer and Normal Proliferating Cells: Two Tales of the Same Name
It has been observed that both cancer tissue cells and normal proliferating cells (NPCs) have the Warburg effect. Our goal here is to demonstrate that they do this for different reasons. To accomplish this, we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in GEO. Our analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect, to raise the intracellular pH from ∼6.8 to ∼7.2 and to prepare for cell division energetically. Once cell cycle starts, the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release, to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH neutral. The cells go back to the normal respiration-based ATP production once the cell division phase ends. In comparison, cancer cells have reached their intracellular pH at ∼7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are repressed. Cancer cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading transporters. Co-expression analyses suggest that lactic acid secretion is regulated by external, non-pH related signals. Overall, our data strongly suggest that the two cell types have the Warburg effect for very different reasons
Deep Learning Analysis and Age Prediction from Shoeprints
Human walking and gaits involve several complex body parts and are influenced
by personality, mood, social and cultural traits, and aging. These factors are
reflected in shoeprints, which in turn can be used to predict age, a problem
not systematically addressed using any computational approach. We collected
100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data
to develop a deep learning end-to-end model ShoeNet to analyze age-related
patterns and predict age. The model integrates various convolutional neural
network models together using a skip mechanism to extract age-related features,
especially in pressure and abrasion regions from pair-wise shoeprints. The
results show that 40.23% of the subjects had prediction errors within 5-years
of age and the prediction accuracy for gender classification reached 86.07%.
Interestingly, the age-related features mostly reside in the asymmetric
differences between left and right shoeprints. The analysis also reveals
interesting age-related and gender-related patterns in the pressure
distributions on shoeprints; in particular, the pressure forces spread from the
middle of the toe toward outside regions over age with gender-specific
variations on heel regions. Such statistics provide insight into new methods
for forensic investigations, medical studies of gait-pattern disorders,
biometrics, and sport studies.Comment: 24 pages, 20 Figure
Ultrasound Versus Contrast-Enhanced Magnetic Resonance Imaging for Subclinical Synovitis and Tenosynovitis: A Diagnostic Performance Study
OBJECTIVES: Radiographic manifestations of synovitis (e.g., erosions) can be observed only in the late stage of rheumatoid arthritis. Ultrasound is a noninvasive, cheap, and widely available technique that enables the evaluation of inflammatory changes in the peripheral joint. In the same way, dynamic contrast-enhanced magnetic resonance imaging (MRI) enables qualitative and quantitative measurements. The objectives of the study were to compare the sensitivity and accuracy of ultrasound in detecting subclinical synovitis and tenosynovitis with those of contrast-enhanced MRI. METHODS: The ultrasonography and contrast-enhanced MRI findings of the wrist, metacarpophalangeal, and proximal interphalangeal joints (n=450) of 75 patients with a history of joint pain and morning stiffness between 6 weeks and 2 years were reviewed. The benefits score was evaluated for each modality. RESULTS: The ultrasonic findings showed inflammation in 346 (77%) joints, while contrast-enhanced MRI found signs of early rheumatoid arthritis in 372 (83%) joints. The sensitivities of ultrasound and contrast-enhanced MRI were 0.795 and 0.855, respectively, and the accuracies were 0.769 and 0.823, respectively. Contrast-enhanced MRI had a likelihood of 0–0.83 and ultrasound had a likelihood of 0–0.77 for detecting synovitis and tenosynovitis at one time. The two imaging modalities were equally competitive for detecting synovitis and tenosynovitis (p=0.055). CONCLUSION: Ultrasound could be as sensitive and specific as contrast-enhanced MRI for the diagnosis of subclinical synovitis and tenosynovitis
Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification
Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer
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