Two-view Graph Neural Networks for Knowledge Graph Completion

We present an effective GNN-based knowledge graph embedding model, named WGE, to capture entity- and relation-focused graph structures. In particular, given the knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. In addition, WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms competitive baselines, obtaining state-of-the-art performances on seven benchmark datasets for knowledge graph completion.Comment: 13 pages; 3 tables; 3 figure

Adsorption of Ammonium (NH4+) Ions onto various Vietnamese biomass residue-derived biochars (wood, rice husk and bamboo)

This study evaluates adsorption of ammonium nitrogen (NH4+-N) ions onto various biochars produced from biomass residues in Vietnam as a function of their physicochemical characteristics. Three biochars, including wood biochar (WBC), rice husk biochar (RBC), and bamboo biochar (BBC), were produced under limited oxygen conditions using Top-Lid Updraft Drum technology at temperatures of 450-550oC. Physicochemical characterization (BET surface area, Cation exchange capacity (CEC), Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy) of the biochars was performed in order to link their porosity and surface functional groups with their NH4+-N capture capacities. Please click on the file below for full content of the abstract

Cluster ionization via two-plasmon excitation

We calculate the two-photon ionization of clusters for photon energies near the surface plasmon resonance. The results are expressed in terms of the ionization rate of a double plasmon excitation, which is calculated perturbatively. For the conditions of the experiment by Schlipper et al., we find an ionization rate of the order of 0.05-0.10 fs^(-1). This rate is used to determine the ionization probability in an external field in terms of the number of photons absorbed and the duration of the field. The probability also depends on the damping rate of the surface plasmon. Agreement with experiment can only be achieved if the plasmon damping is considerably smaller than its observed width in the room-temperature single-photon absorption spectrum.Comment: 17 pages and 6 PostScript figure

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Constructing a robust model that can effectively generalize to test samples under distribution shifts remains a significant challenge in the field of medical imaging. The foundational models for vision and language, pre-trained on extensive sets of natural image and text data, have emerged as a promising approach. It showcases impressive learning abilities across different tasks with the need for only a limited amount of annotated samples. While numerous techniques have focused on developing better fine-tuning strategies to adapt these models for specific domains, we instead examine their robustness to domain shifts in the medical image segmentation task. To this end, we compare the generalization performance to unseen domains of various pre-trained models after being fine-tuned on the same in-distribution dataset and show that foundation-based models enjoy better robustness than other architectures. From here, we further developed a new Bayesian uncertainty estimation for frozen models and used them as an indicator to characterize the model's performance on out-of-distribution (OOD) data, proving particularly beneficial for real-world applications. Our experiments not only reveal the limitations of current indicators like accuracy on the line or agreement on the line commonly used in natural image applications but also emphasize the promise of the introduced Bayesian uncertainty. Specifically, lower uncertainty predictions usually tend to higher out-of-distribution (OOD) performance.Comment: Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation model

Which Is the Most Suitable Classification for Colorectal Cancer, Log Odds, the Number or the Ratio of Positive Lymph Nodes?

Objective: The aim of the current study was to investigate which is the most suitable classification for colorectal cancer, log odds of positive lymph nodes (LODDS) classification or the classifications based on the number of positive lymph nodes (pN) and positive lymph node ratio(LNR) in a Chinese single institutional population. Design: Clinicopathologic and prognostic data of 1297 patients with colorectal cancer were retrospectively studied. The log-rank statistics, Cox’s proportional hazards model, the Nagelkerke R 2 index and a Harrell’s C statistic were used. Results: Univariate and three-step multivariate analyses identified that LNR was a significant prognostic factor and LNR classification was superior to both the pN and LODDS classifications. Moreover, the results of the Nagelkerke R 2 index (0.130) and a Harrell’s C statistic (0.707) of LNR showed that LNR and LODDS classifications were similar and LNR was a little better than the other two classifications. Furthermore, for patients in each LNR classification, prognosis was homologous between those in different pN or LODDS classifications. However, for patients in pN1a, pN1b, LODDS2 and LODDS3 classifications, significant differences in survival were observed among patients in different LNR classifications. Conclusions: For patients with colorectal cancer, the LNR classification is more suitable than pN and LODDS classification

The genetic interactome of prohibitins: coordinated control of cardiolipin and phosphatidylethanolamine by conserved regulators in mitochondria

Prohibitin ring complexes in the mitochondrial inner membrane regulate cell proliferation as well as the dynamics and function of mitochondria. Although prohibitins are essential in higher eukaryotes, prohibitin-deficient yeast cells are viable and exhibit a reduced replicative life span. Here, we define the genetic interactome of prohibitins in yeast using synthetic genetic arrays, and identify 35 genetic interactors of prohibitins (GEP genes) required for cell survival in the absence of prohibitins. Proteins encoded by these genes include members of a conserved protein family, Ups1 and Gep1, which affect the processing of the dynamin-like GTPase Mgm1 and thereby modulate cristae morphogenesis. We show that Ups1 and Gep1 regulate the levels of cardiolipin and phosphatidylethanolamine in mitochondria in a lipid-specific but coordinated manner. Lipid profiling by mass spectrometry of GEP-deficient mitochondria reveals a critical role of cardiolipin and phosphatidylethanolamine for survival of prohibitin-deficient cells. We propose that prohibitins control inner membrane organization and integrity by acting as protein and lipid scaffolds

An Optimized PatchMatch for multi-scale and multi-feature label fusion

Automatic segmentation methods are important tools for quantitative analysis of Magnetic Resonance Images (MRI). Recently, patch-based label fusion approaches have demonstrated state-of-the-art segmentation accuracy. In this paper, we introduce a new patch-based label fusion framework to perform segmentation of anatomical structures. The proposed approach uses an Optimized PAtchMatch Label fusion (OPAL) strategy that drastically reduces the computation time required for the search of similar patches. The reduced computation time of OPAL opens the way for new strategies and facilitates processing on large databases. In this paper, we investigate new perspectives offered by OPAL, by introducing a new multi-scale and multi-feature framework. During our validation on hippocampus segmentation we use two datasets: young adults in the ICBM cohort and elderly adults in the EADC-ADNI dataset. For both, OPAL is compared to state-of-the-art methods. Results show that OPAL obtained the highest median Dice coefficient (89.9% for ICBM and 90.1% for EADC-ADNI). Moreover, in both cases, OPAL produced a segmentation accuracy similar to inter-expert variability. On the EADC-ADNI dataset, we compare the hippocampal volumes obtained by manual and automatic segmentation. The volumes appear to be highly correlated that enables to perform more accurate separation of pathological populations. (C) 2015 Elsevier Inc. All rights reserved.This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). We also thank Tong Tong and Daniel Rueckert for providing us complete results of the methods proposed in Tong et al. (2013), Sonia Tangaro and Marina Boccardi for providing us complete results of the method proposed in Tangaro et al. (2014), and Katherine Gray and Robin Wolz for providing us complete results of the LEAP method proposed in Gray et al. (2014). Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad, NIH grants P30AG010129, K01 AG030514 and the Dana Foundation.Giraud, R.; Ta, V.; Papadakis, N.; Manjón Herrera, JV.; Collins, L.; Coupé Pierrick; Alzheimers Dis Neuroimaging Initia (2016). An Optimized PatchMatch for multi-scale and multi-feature label fusion. NeuroImage. 124(1):770-782. https://doi.org/10.1016/j.neuroimage.2015.07.076S770782124

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

NGHIÊN CỨU NHIỄU LOẠN TẦNG ĐIỆN LY TỨC THỜI BẰNG MÁY THU TÍN HIỆU TẦN SỐ RẤT THẤP TẠI NHA TRANG

Kỹ thuật sử dụng tín hiệu tần số rất thấp (VLF- Very Low Frequency) là một công cụ rất hữu hiệu để nghiên cứu nhiễu loạn tầng điện ly tức thời do tín hiệu này hầu như bị phản xạ tại lớp D tầng điện ly (ở độ cao 60-90 km) khi xãy ra nhiễu loạn. Với yêu câu nhanh chóng nắm bắt các kỹ thuật và công nghệ tiên tiến để làm chủ các thiết bị nghiên cứu, chúng tôi mạnh dạn đề ra mục tiêu tự chế tạo thiết bị phục vụ cho nghiên cứu và ứng dụng. Trong bài báo này, nhóm nghiên cứu xin trình bày kết quả ứng dụng máy thu tín hiệu VLF được chế tạo tại Nha Trang để nghiên cứu nhiễu loạn tầng điện ly tức thời cho mục đích giáo dục thời tiết không gian