848 research outputs found
l-connectivity, l-edge-connectivity and spectral radius of graphs
Let G be a connected graph. The toughness of G is defined as
t(G)=min{\frac{|S|}{c(G-S)}}, in which the minimum is taken over all proper
subsets S\subset V(G) such that c(G-S)\geq 2 where c(G-S) denotes the number of
components of G-S. Confirming a conjecture of Brouwer, Gu [SIAM J. Discrete
Math. 35 (2021) 948--952] proved a tight lower bound on toughness of regular
graphs in terms of the second largest absolute eigenvalue. Fan, Lin and Lu
[European J. Combin. 110 (2023) 103701] then studied the toughness of simple
graphs from the spectral radius perspective. While the toughness is an
important concept in graph theory, it is also very interesting to study |S| for
which c(G-S)\geq l for a given integer l\geq 2. This leads to the concept of
the l-connectivity, which is defined to be the minimum number of vertices of G
whose removal produces a disconnected graph with at least l components or a
graph with fewer than l vertices. Gu [European J. Combin. 92 (2021) 103255]
discovered a lower bound on the l-connectivity of regular graphs via the second
largest absolute eigenvalue. As a counterpart, we discover the connection
between the l-connectivity of simple graphs and the spectral radius. We also
study similar problems for digraphs and an edge version
Kerr-nonlinearity Enhanced Conventional Photon Blockade in Second-order-nonlinear System
In the recent publication [Phys. Rev. B 87, 235319 (2013)], the conventional
photon blockade(CPB) was studied for the low frequency mode in a second-order
nonlinear system. In this paper, we will study the CPB for the high frequency
mode in a second-order nonlinear system with the Kerr nonlinearity filling in
the low-frequency cavity. By solving the master equation and calculating the
zero-delay-time second order correlation function , strong photon
antibunching can be obtained in the high frequency cavity. The optimal
condition for strong antibunching is found by analyticcal culations and
discussions of the optimal condition are presented. We find that the
Kerr-nonlinearities can enhanced the CPB effect. In addition, this scheme is
not sensitive to the reservoir temperature, which make the current system
easier to implement experimentally
Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control
High-resolution tactile sensing can provide accurate information about local
contact in contact-rich robotic tasks. However, the deployment of such tasks in
unstructured environments remains under-investigated. To improve the robustness
of tactile robot control in unstructured environments, we propose and study a
new concept: \textit{tactile saliency} for robot touch, inspired by the human
touch attention mechanism from neuroscience and the visual saliency prediction
problem from computer vision. In analogy to visual saliency, this concept
involves identifying key information in tactile images captured by a tactile
sensor. While visual saliency datasets are commonly annotated by humans,
manually labelling tactile images is challenging due to their counterintuitive
patterns. To address this challenge, we propose a novel approach comprised of
three interrelated networks: 1) a Contact Depth Network (ConDepNet), which
generates a contact depth map to localize deformation in a real tactile image
that contains target and noise features; 2) a Tactile Saliency Network
(TacSalNet), which predicts a tactile saliency map to describe the target areas
for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen),
which generates noise features to train the TacSalNet. Experimental results in
contact pose estimation and edge-following in the presence of distractors
showcase the accurate prediction of target features from real tactile images.
Overall, our tactile saliency prediction approach gives robust sim-to-real
tactile control in environments with unknown distractors. Project page:
https://sites.google.com/view/tactile-saliency/.Comment: Accepted by IROS 202
Modulation of hippocampal gamma oscillations by dopamine in heterozygous Reeler mice In vitro
The reelin haploinsufficient heterozygous reeler mouse (HRM), an animal model of schizophrenia, has altered mesolimbic dopaminergic pathways, shares similar neurochemical, and behavioral properties with the patients with schizophrenia. Dysfunctional neural circuitry with impaired gamma (γ) oscillation (30–80 Hz) has been implicated in abnormal cognition in patients with schizophrenia. However, the function of neural circuitry in terms of γ oscillation and its modulation by dopamine has not been reported in HRM. In this study, first, we recorded γ oscillations in CA3 from wide type (WT) mice and HRM hippocampal slices, and studied the effects of dopamine (DA) on γ oscillations. We found that there was no difference in γ power between WT mice and HRM and that dopamine increased γ power of WT mice but not HRM, suggesting that dopamine modulations of network oscillations in HRM are impaired. Second, we found that N-methyl-D-aspartate receptor (NMDAR) antagonist itself increased γ power and occluded DA-mediated enhancement of γ power in WT mice but partially restored DA modulation of γ oscillations in HRM. Third, inhibition of phosphoinositide 3-kinase (PI3K), a downstream molecule of NMDAR, increased γ power and blocked the effects of DA on γ oscillation in WT mice and had no significant effect on γ power but largely restored DA modulation of γ oscillations in HRM. Our results reveal that impaired DA function in HRM is associated with dysregulated NMDAR-PI3K signaling, a mechanism that may be relevant in the pathology of schizophrenia
Numerical model for geothermal energy utilization from double pipe heat exchanger in abandoned oil wells
The number of abandonded wells are increasing in the late period of oilfield development. The utilization of these abandonded oil wells is promising and environment-friendly for geothermal development. In this study, a numerical model for geothermal heating is derived from a double pipe heat exchanger in abandoned oil wells. The main influencing factors of injection rate, injection time, and the types of filler in casing annulus on temperature profiles and outlet temperature have been considered in this model. The influences of injection rate on heat-mining rate are then discussed. Results show that the double pipe heat exchanger can gain higher temperature at the outlet when the casing annulus is filled by liquid other than dry cement under the given parameter combination. The outlet temperature decreases with the increase in injection rate and injection time. The temperature rapidly decreases in the first 40 days during the injection process. The balance between heat mining rate and outlet temperature is important for evaluating a double pipe heat exchanger in abandoned oil wells. This work may provide a useful tool for a field engineer to estimate the temperature of liquid in wellhead and evaluate the heat transfer efficiency for double pipe heat exchanger in abandoned oil wells.Cited as: Lin, Z., Liu, K., Liu, J., Geng, D., Ren, K., Zheng, Z. Numerical model for geothermal energy utilization from double pipe heat exchanger in abandoned oil wells. Advances in Geo-Energy Research, 2021, 5(2): 212-221, doi: 10.46690/ager.2021.02.1
Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy
(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer
Triglyceride-rich lipoproteins and cardiovascular diseases
The global prevalence of cardiovascular diseases (CVD) continues to rise steadily, making it a leading cause of mortality worldwide. Atherosclerosis (AS) serves as a primary driver of these conditions, commencing silently at an early age and culminating in adverse cardiovascular events that severely impact patients’ quality of life or lead to fatality. Dyslipidemia, particularly elevated levels of low-density lipoprotein cholesterol (LDL-C), plays a pivotal role in AS pathogenesis as an independent risk factor. Research indicates that abnormal LDL-C accumulation within arterial walls acts as a crucial trigger for atherosclerotic plaque formation. As the disease progresses, plaque accumulation may rupture or dislodge, resulting in thrombus formation and complete blood supply obstruction, ultimately causing myocardial infarction, cerebral infarction, and other common adverse cardiovascular events. Despite adequate pharmacologic therapy targeting LDL-C reduction, patients with cardiometabolic abnormalities remain at high risk for disease recurrence, highlighting the importance of addressing lipid risk factors beyond LDL-C. Recent attention has focused on the causal relationship between triglycerides, triglyceride-rich lipoproteins (TRLs), and their remnants in AS risk. Genetic, epidemiologic, and clinical studies suggest a causal relationship between TRLs and their remnants and the increased risk of AS, and this dyslipidemia may be an independent risk factor for adverse cardiovascular events. Particularly in patients with obesity, metabolic syndrome, diabetes, and chronic kidney disease, disordered TRLs and its remnants levels significantly increase the risk of atherosclerosis and cardiovascular disease development. Accumulation of over-synthesized TRLs in plasma, impaired function of enzymes involved in TRLs lipolysis, and impaired hepatic clearance of cholesterol-rich TRLs remnants can lead to arterial deposition of TRLs and its remnants, promoting foam cell formation and arterial wall inflammation. Therefore, understanding the pathogenesis of TRLs-induced AS and targeting it therapeutically could slow or impede AS progression, thereby reducing cardiovascular disease morbidity and mortality, particularly coronary atherosclerotic heart disease
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