Proteomics-based analysis of differentially expressed proteins in the CXCR1-knockdown gastric carcinoma MKN45 cell line and its parental cell

C-X-C chemokine receptor types 1 (CXCR1), a cell-surface G-protein-coupled receptor has been found to be associated with tumorigenesis, development, and progression of some tumors. Previously, we have found that CXCR1 overexpression is associated with late-stage gastric adenocarcinoma. We also have demonstrated that knockdown of CXCR1 could inhibit cell proliferation in vitro and in vivo. In this study, we compared the changes of protein expression profile between gastric carcinoma MKN45 cell line and CXCR1-knockdown MKN45 cell line by 2D electrophoresis. Among the 101 quantified proteins, 29 spots were significantly different, among which 13 were downregulated and 16 were up-regulated after CXCR1 knockdown. These proteins were further identified by mass spectrometry analysis. Among them, several up-regulated proteins such as hCG2020155, Keratin8, heterogeneous nuclear ribonucleoprotein C (C1/C2), and several downregulated proteins such as Sorcin, heat shock protein 27, serpin B6 isoform b, and heterogeneous nuclear ribonucleoprotein K were confirmed. These proteins are related to cell cycle, the transcription regulation, cell adherence, cellular metabolism, drug resistance, and so on. These results provide an additional support to the hypothesis that CXCR1 might play an important role in proliferation, invasion, metastasis, and prognosis, and drug resistance of gastric carcinoma

Surface Ni-rich engineering towards highly stable Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 cathode materials

Abstract(#br)Li-rich layered oxide cathode materials (LLOs) are regarded as promising next-generation cathode candidate in high-energy-density lithium ion batteries due to their high specific capacity over 250 mA h g −1 . However, LLOs always suffer from a series of severe issues, such as rapid voltage fading, fast capacity decay and bad cycling stability. In this work, Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 -Li 1.2 Mn 0.44 Ni 0.32 Co 0.04 O 2 (LLO-111@111/811) hybrid layered-layered cathode is constructed via facilely increasing surface Ni content. Profiting from this special design, the prepared LLO-111@111/811 cathode exhibits a remarkable specific capacity of 249 mA h g −1 with a high capacity retention of 89.3% and a high discharge voltage of 3.57 V with a voltage retention of 83.0% after cycling 350 times at 0.5 C. As a result, the specific energy of LLO-111@111/811 cathode is 887 Wh Kg −1 at 0.5 C and it keeps as high as 658 Wh Kg −1 after 350 cycles. LLO-111@111/811 also exhibits an initial high capacity of 169 mA h g −1 at a high rate of 5 C and maintains a good capacity retention of 90.0% after 200 cycles. This strategy can successfully improve structural stability, suppress capacity decay and restrain voltage fading of LLOs, which is beneficial for their practical application

Depression care management for late-life depression in China primary care: Protocol for a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>As a major public health issue in China and worldwide, late-life depression is associated with physical limitations, greater functional impairment, increased utilization and cost of health care, and suicide. Like other chronic diseases in elders such as hypertension and diabetes, depression is a chronic disease that the new National Health Policy of China indicates should be managed in primary care settings. Collaborative care, linking primary and mental health specialty care, has been shown to be effective for the treatment of late-life depression in primary care settings in Western countries. The primary aim of this project is to implement a depression care management (DCM) intervention, and examine its effectiveness on the depressive symptoms of older patients in Chinese primary care settings.</p> <p>Methods/Design</p> <p>The trial is a multi-site, primary clinic based randomized controlled trial design in Hangzhou, China. Sixteen primary care clinics will be enrolled in and randomly assigned to deliver either DCM or care as usual (CAU) (8 clinics each) to 320 patients (aged ≥ 60 years) with major depression (20/clinic; n = 160 in each treatment condition). In the DCM arm, primary care physicians (PCPs) will prescribe 16 weeks of antidepressant medication according to the treatment guideline protocol. Care managers monitor the progress of treatment and side effects, educate patients/family, and facilitate communication between providers; psychiatrists will provide weekly group psychiatric consultation and CM supervision. Patients in both DCM and CAU arms will be assessed by clinical research coordinators at baseline, 4, 8, 12, 18, and 24 months. Depressive symptoms, functional status, treatment stigma and clients' satisfaction will be used to assess patients' outcomes; and clinic practices, attitudes/knowledge, and satisfaction will be providers' outcomes.</p> <p>Discussion</p> <p>This will be the first trial of the effectiveness of a collaborative care intervention aiming to the management of late-life depression in China primary care. If effective, its finding will have relevance to policy makers who wish to scale up DCM treatments for late-life depression in national wide primary care across China.</p> <p>Study Registration</p> <p>The DCM project is registered through the National Institutes of Health sponsored by clinical trials registry and has been assigned the identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01287494">NCT01287494</a></p

LDPTrace: Locally Differentially Private Trajectory Synthesis

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privay concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios due to poor utility, dependence on external knowledge, high computational overhead, and vulnerability to attacks. To address these limitations, we introduce LDPTrace, a novel locally differentially private trajectory synthesis framework. Our framework takes into account three crucial patterns inferred from users' trajectories in the local setting, allowing us to synthesize trajectories that closely resemble real ones with minimal computational cost. Additionally, we present a new method for selecting a proper grid granularity without compromising privacy. Our extensive experiments using real-world data, various utility metrics and attacks, demonstrate the efficacy and efficiency of LDPTrace.Comment: Accepted by VLDB 2023. Code is available: https://github.com/zealscott/LDPTrac

Self-guided learning to denoise for robust recommendation

The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to various kinds of recommendation models. In this paper, we thoroughly investigate the memorization effect of recommendation models, and propose a new denoising paradigm, i.e., Self-Guided Denoising Learning (SGDL), which is able to collect memorized interactions at the early stage of the training (i.e., "noise-resistant" period), and leverage those data as denoising signals to guide the following training (i.e., "noise-sensitive" period) of the model in a meta-learning manner. Besides, our method can automatically switch its learning phase at the memorization point from memorization to self-guided learning, and select clean and informative memorized data via a novel adaptive denoising scheduler to improve the robustness. We incorporate SGDL with four representative recommendation models (i.e., NeuMF, CDAE, NGCF and LightGCN) and different loss functions (i.e., binary cross-entropy and BPR loss). The experimental results on three benchmark datasets demonstrate the effectiveness of SGDL over the state-of-the-art denoising methods like T-CE, IR, DeCA, and even state-of-the-art robust graph-based methods like SGCN and SGL.Comment: Accepted by SIGIR202