Similarities and differences of functional connectivity in drug-naïve, first-episode adolescent and young adult with major depressive disorder and schizophrenia

Major depressive disorder (MDD) and schizophrenia (SZ) are considered two distinct psychiatric disorders. Yet, they have considerable overlap in symptomatology and clinical features, particularly in the initial phases of illness. The amygdala and prefrontal cortex (PFC) appear to have critical roles in these disorders; however, abnormalities appear to manifest differently. In our study forty-nine drug-naïve, first-episode MDD, 45 drug-naïve, first-episode SZ, and 50 healthy control (HC) participants from 13 to 30 years old underwent resting-state functional magnetic resonance imaging. Functional connectivity (FC) between the amygdala and PFC was compared among the three groups. Significant differences in FC were observed between the amygdala and ventral PFC (VPFC), dorsolateral PFC (DLPFC), and dorsal anterior cingulated cortex (dACC) among the three groups. Further analyses demonstrated that MDD showed decreased amygdala-VPFC FC and SZ had reductions in amygdala-dACC FC. Both the diagnostic groups had significantly decreased amygdala-DLPFC FC. These indicate abnormalities in amygdala-PFC FC and further support the importance of the interaction between the amygdala and PFC in adolescents and young adults with these disorders. Additionally, the alterations in amygdala-PFC FC may underlie the initial similarities observed between MDD and SZ and suggest potential markers of differentiation between the disorders at first onset

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark

Software-defined mobility management: Architecture proposal and future directions

A common characteristic for all of the uses in 5G wireless networks is the ubiquity and the almost permanent connection to the mobile network to get access to external applications. This really imposes a challenge in the signaling procedures provided to get track of the user and to guarantee session continuity. The mobility management mechanisms will play a central role in the 5G networks because of the always-on connectivity demand. This article presents a software defined approach to mobility management procedures addressing the present challenges and proposing some future directions for a more efficient service provision and a better usage of the network resources. The feasibility of such a Software-Defined Mobility Management architecture is assessed in a specific test-bed

Decoupling Information and Connectivity via Information-Centric Transport

The power of Information-Centric Networking (ICN) architectures lies in their abstraction for communication --- the request for named data. This abstraction promises that applications can choose to operate only in the information plane, agnostic to the mechanisms implemented in the connectivity plane. However, despite this powerful promise, the information and connectivity planes are presently coupled in today\u27s incarnations of leading ICNs by a core architectural component, the forwarding strategy. Presently, this component is not sustainable: it implements both the information and connectivity mechanisms without specifying who should choose a forwarding strategy --- an application developer or the network operator. In practice, application developers can specify a strategy only if they understand connectivity details, while network operators can assign strategies only if they understand application expectations. In this paper, we define the role of forwarding strategies, and we introduce Information-Centric Transport (ICT) as an abstraction for cleanly decoupling the information plane from the connectivity plane. We discuss how ICTs allow applications to operate in the information plane, concerned only with namespaces and trust identities, leaving network node operators free to deploy whatever strategy mechanisms make sense for the connectivity that they manage. To illustrate the ICT concept, we demonstrate ICT-Sync and ICT-Notify. We show how these ICTs 1) enable applications to operate regardless of connectivity details, 2) are designed to satisfy a predefined set of application requirements and are free from application-specifics, and 3) can be deployed by network operators where needed, without requiring any change to the application logic

Pan-cancer and single-cell analysis reveals FAM83D expression as a cancer prognostic biomarker

Background: The family with sequence similarity 83 member D (FAM83D) protein is known to play a significant role in many human diseases. However, its role in cancer remains ambiguous. This study aimed to investigate the function of FAM83D in a pan-cancer analysis, with a special focus on breast cancer.Methods: Samples were collected from The Cancer Genome Atlas (TCGA) and used for bioinformatic analysis. Datasets from the Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx) databases were also analyzed for verification. The potential value of FAM83D as a prognostic and diagnostic biomarker was visualized through R software. The “survival” and “GSVA” package were used for univariate, multivariate and pathway enrichment analyseis. We further analyzed the CancerSEA databases and TISIDB websites for single-cell and immune-related profiling. Lastly, we validated those data in vitro using quantitative reverse transcriptase-polymerase chain reaction (RT‒qPCR), cell counting kit-8 (CCK-8), transwell, flow cytometry, and tumorigenicity assays in a murine cell line model.Results: The expression of FAM83D in tumor samples was significantly higher than in normal tissues for most cancer types in the datasets. We confirmed this finding using RT‒qPCR in a breast cancer cell line. Analysis of multiple datasets suggests that overall survival (OS) was extremely poor for breast cancer patients with high FAM83D expression. The CCK-8 assay demonstrated that MCF-7 cell proliferation was inhibited after genetic silencing of FAM83D. Transwell assay showed that knockdown of FAM83D significantly inhibited the invasion and migration ability of MCF-7 cells compared to the control. The results of flow cytometry showed that silencing FAM83D could block the G1 phase of MCF-7 cells compared with negative groups. The tumorigenicity assay in nude mice indicated that the tumorigenic ability to silence FAM83D decreased compared.Conclusion: Results suggest that FAM83D expression can serve as a valuable biomarker and core gene across cancer types. Furthermore, FAM83D expression is significantly associated with MCF-7 cell proliferation and thus may be a prospective prognostic biomarker especially for breast cancer