Intrusion of polyethylene glycol into solid-state nanopores

The intrusion of PEG aqueous solution into solid-state-nanopores upon mechanical pressure is experimentally investigated. By using hydrophobic nanoporous silica with a broad range of pore sizes, the characteristic size of PEG chains in water while penetrating nanopores is measured and analyzed, which increases with molecular weight and decreases with concentration of PEG. Its sensitivity to molecular weight is relatively limited due to nano-confinement. The inclusion of PEG as an intruding liquid imposes a rate effect on the intrusion pressure, and inhibits the extrusion from the nanopores

Elastomeric cellular structure enhanced by compressible liquid filler

Elastomeric cellular structures provide a promising solution for energy absorption. Their flexible and resilient nature is particularly relevant to protection of human bodies. Herein we develop an elastomeric cellular structure filled with nanoporous material functionalized (NMF) liquid. Due to the nanoscale infiltration in NMF liquid and its interaction with cell walls, the cellular structure has a much enhanced mechanical performance, in terms of loading capacity and energy absorption density. Moreover, it is validated that the structure is highly compressible and self-restoring. Its hyper-viscoelastic characteristics are elucidated

Rate effect of liquid infiltration into mesoporous materials

Rate effect of liquid infiltration in mesopores is associated with both liquid viscosity and the solid–liquid interfacial effect.</p

Localized Sparse Incomplete Multi-view Clustering

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a l1 norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC.Comment: Published in IEEE Transactions on Multimedia (TMM). The code is available at Github https://github.com/justsmart/LSIMV

Information Recovery-Driven Deep Incomplete Multiview Clustering Network

Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.Comment: Accepted by TNNLS 2023. Please contact me if you have any questions: [email protected]. The code is available at: https://github.com/justsmart/RecForme

Protective Effect of Calculus Bovis Sativus

Calculus Bovis Sativus (CBS) is a commonly used traditional Chinese medicine, which has been reported to exhibit antispasmodic, fever-reducing, anti-inflammatory, and gallbladder-repairing effects. The present study aims to investigate the protective effect of CBS on dextran sulphate sodium- (DSS-) induced ulcerative colitis (UC) in mice. C57BL/6 male mice were exposed to 5% DSS in drinking water. CBS was given orally at 50 and 150 mg/kg once per day for 7 days. Body weight, disease activity index (DAI), colon length, colonic myeloperoxidase (MPO) activity, superoxide dismutase (SOD) activity, and malondialdehyde (MDA) and nitric oxide (NO) levels were measured. Administration of CBS significantly reserved these changes, decreased the MPO activity and MDA and NO level, and increased the SOD activity in the colon tissue. Histological observation suggested that CBS alleviated edema, mucosal damage, and inflammatory cells infiltration induced by DSS in the colon. Moreover, CBS significantly downregulated the mRNA expression of tumor necrosis factor-α (TNF-α), interleukin- (IL-) 1β and IL-6 in the colon tissue. Our data suggested that CBS exerted protective effect on DSS-induced UC partially through the antioxidant and anti-inflammatory activities

Segmental abnormalities of superior longitudinal fasciculus microstructure in patients with schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder: An automated fiber quantification tractography study

IntroductionSuperior longitudinal fasciculus (SLF) is a white matter (WM) tract that connects the frontal, parietal and temporal lobes. SLF integrity has been widely assessed in neuroimaging studies of psychiatric disorders, such as schizophrenia (SZ), bipolar disorder (BD), and attention-deficit/hyperactivity disorder (ADHD). However, prior studies have revealed inconsistent findings and comparisons across disorders have not been fully examined.MethodsHere, we obtained data for 113 patients (38 patients with SZ, 40 with BD, 35 with ADHD) and 94 healthy controls from the UCLA Consortium for Neuropsychiatric Phenomic LA5c dataset. We assessed the integrity of 20 major WM tracts with a novel segmentation method by automating fiber tract quantification (AFQ). The AFQ divides each tract into 100 equal parts along the direction of travel, with fractional anisotropy (FA) of each part taken as a characteristic. Differences in FA among the four groups were examined.ResultsCompared to healthy controls, patients with SZ showed significantly lower FA in the second half (51–100 parts) of the SLF. No differences were found between BD and healthy controls, nor between ADHD and healthy controls. Results also demonstrated that patients with SZ showed FA reduction in the second half of the SLF relative to patients with BP. Moreover, greater FA in patients in SLF was positively correlated with the manic-hostility score of the Brief Psychiatry Rating scale.DiscussionThese findings indicated that differences in focal changes in SLF might be a key neurobiological abnormality contributing to characterization of these psychiatric disorders

Efficacy and Safety of Everolimus for Maintenance Immunosuppression of Kidney Transplantation: A Meta-Analysis of Randomized Controlled Trials

Background Conversion to everolimus is often used in kidney transplantation to overcome calcineurin inhibitor (CNI) nephrotoxicity but there is conflicting evidence for this approach. Objectives To investigate the benefits and harm from randomized clinical trials (RCTs) involving the conversion from CNI to everolimus after kidney transplantation. Methods Databases were searched up to March 2016. Two reviewers independently assessed trials for eligibility and quality, and extracted data. Results are expressed as risk ratio (RR) or mean difference (MD) with 95% confidence intervals (CI). Results Eleven RCTs, with a total of 1,633 patients, met the final inclusion criteria. Patients converted to everolimus had improved renal function at 1 year posttransplant with an estimated glomerular filtration rate (eGFR) of 5.36 mL/min per 1.73 m2 greater than patients remaining on CNI (p = 0.0005) and the longer-term results (\u3e 1 year) of renal function was identical to that of 1 year. There was not a substantial difference in graft loss, mortality, and the occurrence of adverse events (AEs) or serious AEs. However, the risks of acute rejection and trial termination due to AEs with everolimus are respectively 1.82 and 2.63 times greater than patients staying on CNI at 1 year posttransplant (p = 0.02, p = 0.03, respectively). Further, those patients who converted to everolimus had a substantially greater risk of anemia, hyperlipidemia, hypercholesterolemia, hypokalemia, proteinuria, stomatitis, mouth ulceration, and acne. Conclusions Conversion from CNI to everolimus after kidney transplantation is associated with improved renal function in the first 5 years posttransplant but increases the risk of acute rejection at 1 year posttransplant and may not be well endured