Multiple freshwater invasions of the tapertail anchovy (Clupeiformes: Engraulidae) of the Yangtze River.

Freshwater fish evolved from anadromous ancestors can be found in almost all continents. The roles of paleogeographic events and nature selection in speciation process often are under focus of research. We studied genetic diversity of anadromous and resident tapertail anchovies (Coilia nasus species complex) in the Yangtze River Basin using 4,434 nuclear loci, and tested the history of freshwater invasion of C. nasus. We found that both C. brachygnathus and C. nasus were valid species, but the resident C. nasus taihuensis and the anadromous C. nasus were not different genetically based on Bayes factor species delimitation (BFD*). Maximum likelihood tree, Network, PCA and STRUCTURE analyses all corroborated the results of BFD*. Two independent freshwater invasion events of C. nasus were supported, with the first event occurring around 4.07 Ma and the second happened around 3.2 Ka. The time of the two freshwater invasions is consistent with different paleogeographic events. Estimation showed that gene flow was higher within ecotypes than between different ecotypes. F-DIST analyses identified 120 disruptive outliers by comparing C. brachygnathus to anadromous C. nasus, and 21 disruptive outliers by comparing resident C. nasus to anadromous C. nasus. Nine outliers were found to be common between the two comparisons, indicating that independent freshwater invasion of C. nasus might involve similar molecular pathways. The results of this study suggest that adaptation to landlocked freshwater environment of migratory fish can evolve multiple times independently, and morphology of landlocked ecotypes may cause confusion in their taxonomy

Far-Infrared Spectroscopy of Cationic Polycyclic Aromatic Hydrocarbons: Zero Kinetic Energy Photoelectron Spectroscopy of Pentacene Vaporized from Laser Desorption

doi:10.1088/0004-637X/715/1/485The distinctive set of infrared (IR) emission bands at 3.3, 6.2, 7.7, 8.6, and 11.3 μm are ubiquitously seen in a wide variety of astrophysical environments. They are generally attributed to polycyclic aromatic hydrocarbon (PAH) molecules. However, not a single PAH species has yet been identified in space, as the mid-IR vibrational bands are mostly representative of functional groups and thus do not allow one to fingerprint individual PAH molecules. In contrast, the far-IR (FIR) bands are sensitive to the skeletal characteristics of a molecule, hence they are important for chemical identification of unknown species. With an aim to offer laboratory astrophysical data for the Herschel Space Observatory, Stratospheric Observatory for Infrared Astronomy, and similar future space missions, in this work we report neutral and cation FIR spectroscopy of pentacene (C22H14), a five-ring PAH molecule. We report three IR active modes of cationic pentacene at 53.3, 84.8, and 266 μm that may be detectable by space missions such as the SAFARI instrument on board SPICA. In the experiment, pentacene is vaporized from a laser desorption source and cooled by a supersonic argon beam. We have obtained results from two-color resonantly enhanced multiphoton ionization and two-color zero kinetic energy photoelectron (ZEKE) spectroscopy. Several skeletal vibrational modes of the first electronically excited state of the neutral species and those of the cation are assigned, with the aid of ab initio and density functional calculations. Although ZEKE is governed by the Franck-Condon principle different from direct IR absorption or emission, vibronic coupling in the long ribbon-like molecule results in the observation of a few IR active modes. Within the experimental resolution of ~7 cm-1, the frequency values from our calculation agree with the experiment for the cation, but differ for the electronically excited intermediate state. Consequently, modeling of the intensity distribution is difficult and may require explicit inclusion of vibronic interactions.This work is supported by the National Aeronautics and Space Administration under award No. NNX09AC03G. A.L. is supported in part by the NSF grant AST 07-07866, a Spitzer Theory grant and a Herschel Theory grant

Strongly Nonlinear Topological Phases of Cascaded Topoelectrical Circuits

Circuits provide ideal platforms of topological phases and matter, yet the study of topological circuits in the strongly nonlinear regime, has been lacking. We propose and experimentally demonstrate strongly nonlinear topological phases and transitions in one-dimensional electrical circuits composed of nonlinear capacitors. Nonlinear topological interface modes arise on domain walls of the circuit lattices, whose topological phases are controlled by the amplitudes of nonlinear voltage waves. Experimentally measured topological transition amplitudes are in good agreement with those derived from nonlinear topological band theory. Our prototype paves the way towards flexible metamaterials with amplitude-controlled rich topological phases and is readily extendable to two and three-dimensional systems that allow novel applications.Comment: accepted by Frontiers of Physics, 18+9 pages, 4+3 figure

DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization

Data users need relevant context and research expertise to effectively search for and identify relevant datasets. Leading data providers, such as the Inter-university Consortium for Political and Social Research (ICPSR), offer standardized metadata and search tools to support data search. Metadata standards emphasize the machine-readability of data and its documentation. There are opportunities to enhance dataset search by improving users' ability to learn about, and make sense of, information about data. Prior research has shown that context and expertise are two main barriers users face in effectively searching for, evaluating, and deciding whether to reuse data. In this paper, we propose a novel chatbot-based search system, DataChat, that leverages a graph database and a large language model to provide novel ways for users to interact with and search for research data. DataChat complements data archives' and institutional repositories' ongoing efforts to curate, preserve, and share research data for reuse by making it easier for users to explore and learn about available research data.Comment: 6 pages, 2 figures, and 1 table. Accepted to the 86th Annual Meeting of the Association for Information Science & Technolog

LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization

Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages. However, previous work on FRS performs similarity search via inner product in continuous embedding space, which causes an efficiency bottleneck when the scale of items is extremely large. We argue that such a scheme in federated settings ignores the limited capacities in resource-constrained user devices (i.e., storage space, computational overhead, and communication bandwidth), and makes it harder to be deployed in large-scale recommender systems. Besides, it has been shown that transmitting local gradients in real-valued form between server and clients may leak users' private information. To this end, we propose a lightweight federated recommendation framework with privacy-preserving matrix factorization, LightFR, that is able to generate high-quality binary codes by exploiting learning to hash technique under federated settings, and thus enjoys both fast online inference and economic memory consumption. Moreover, we devise an efficient federated discrete optimization algorithm to collaboratively train model parameters between the server and clients, which can effectively prevent real-valued gradient attacks from malicious parties. Through extensive experiments on four real-world datasets, we show that our LightFR model outperforms several state-of-the-art FRS methods in terms of recommendation accuracy, inference efficiency and data privacy.Comment: Accepted by ACM Transactions on Information Systems (TOIS

Prevalence of polypharmacy and potentially inappropriate medication use in older lung cancer patients: A systematic review and meta-analysis

Objectives: In older lung cancer patients, polypharmacy and the use of potentially inappropriate medications (PIMs) are commonly reported, but no systematic review or meta-analysis has been carried out to ascertain the prevalence and risk variables in this group. This study aimed to identify the prevalence of polypharmacy, PIMs and associated risk variables in older lung cancer patients.Methods: We searched for articles from the beginning to February 2022 in PubMed, Embase, and Web of Science that related the use of PIMs and polypharmacy by older lung cancer patients (PROSPERO Code No: CRD42022311603). Meta-analysis was performed on observational studies describing the prevalence and correlation of polypharmacy or PIMs in older patients with lung cancer.Results: Of the 387 citations, 6 articles involving 16,890 patients were included in the final sample. In older lung cancer patients pooled by meta-analysis, 38% and 35% of PIMs and polypharmacy, respectively. The prevalence of PIMs was 43%, 49%, and 28%, respectively, according to the 2019 AGS Beers criteria, 2014 screening tool for older people’s prescriptions/screening tool for alerting to the proper therapy (STOPP/START criteria) criteria, and other criteria.Conclusion: This systematic review and meta-analysis demonstrated a high prevalence of polypharmacy and PIMs among older lung cancer patients. Therefore, it is essential to take rational interventions for older lung cancer patients to receive reasonable pharmacotherapy.Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD42022311603]

GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials

Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: over-smoothing, over-fitting, difficult to train, and strong homophily assumption. For example, Simple Graph Convolution (SGC) is known to suffer from the first and fourth limitations. To tackle these limitations, we identify a set of key designs including (D1) dilated convolution, (D2) multi-channel learning, (D3) self-attention score, and (D4) sign factor to boost learning from different types (i.e. homophily and heterophily) and scales (i.e. small, medium, and large) of networks, and combine them into a graph neural network, GPNet, a simple and efficient one-layer model. We theoretically analyze the model and show that it can approximate various graph filters by adjusting the self-attention score and sign factor. Experiments show that GPNet consistently outperforms baselines in terms of average rank, average accuracy, complexity, and parameters on semi-supervised and full-supervised tasks, and achieves competitive performance compared to state-of-the-art model with inductive learning task.Comment: 15 pages, 15 figure

Air quality data clustering using EPLS method

[EN] Nowadays air quality data can be easily accumulated by sensors around the world. Analysis on air quality data is very useful for society decision. Among five major air pollutants which are calculated for AQI (Air Quality Index), PM2.5 data is the most concerned by the people. PM2.5 data is also cross-impacted with the other factors in the air and which has properties of non-linear non-stationary including high noise level and outlier. Traditional methods cannot solve the problem of PM2.5 data clustering very well because of their inherent characteristics. In this paper, a novel model-based feature extraction method is proposed to address this issue. The EPLS model includes: (1) Mode Decomposition, in which EEMD algorithm is applied to the aggregation dataset; (2) Dimension Reduction, which is carried out for a more significant set of vectors; (3) Least Squares Projection, in which all testing data are projected to the obtained vectors. Synthetic dataset and air quality dataset are applied to different clustering methods and similarity measures. Experimental results demonstrate that EPLS is efficient in dealing with high noise level and outlier air quality clustering problems, and which can also be adapted to various clustering techniques and distance measures. (C) 2016 Elsevier B.V. All rights reserved.This work was supported in part by the National Natural Science Foundation of China (Nos. 61440018, 61501411), the Hubei Natural Science Foundation (No. 2014CFB904), China Scholarship Council Funding.Chen, Y.; Wang, L.; Li, F.; Du, B.; Choo, KR.; Hassan Mohamed, H.; Qin, W. (2017). Air quality data clustering using EPLS method. Information Fusion. 36:225-232. https://doi.org/10.1016/j.inffus.2016.11.015S2252323

Attenuation of osteoarthritis via blockade of the SDF-1/CXCR4 signaling pathway

This study was performed to evaluate the attenuation of osteoarthritic (OA) pathogenesis via disruption of the stromal cell-derived factor-1 (SDF-1)/C-X-C chemokine receptor type 4 (CXCR4) signaling with AMD3100 in a guinea pig OA model. OA chondrocytes and cartilage explants were incubated with SDF-1, siRNA CXCR4, or anti-CXCR4 antibody before treatment with SDF-1. Matrix metalloproteases (MMPs) mRNA and protein levels were measured with real-time polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The 35 9-month-old male Hartley guinea pigs (0.88 kg ± 0.21 kg) were divided into three groups: AMD-treated group (n = 13); OA group (n = 11); and sham group (n = 11). At 3 months after treatment, knee joints, synovial fluid, and serum were collected for histologic and biochemical analysis. The severity of cartilage damage was assessed by using the modified Mankin score. The levels of SDF-1, glycosaminoglycans (GAGs), MMP-1, MMP-13, and interleukin-1 (IL-1β) were quantified with ELISA. SDF-1 infiltrated cartilage and decreased proteoglycan staining. Increased glycosaminoglycans and MMP-13 activity were found in the culture media in response to SDF-1 treatment. Disrupting the interaction between SDF-1 and CXCR4 with siRNA CXCR4 or CXCR4 antibody attenuated the effect of SDF-1. Safranin-O staining revealed less cartilage damage in the AMD3100-treated animals with the lowest Mankin score compared with the control animals. The levels of SDF-1, GAG, MMP1, MMP-13, and IL-1β were much lower in the synovial fluid of the AMD3100 group than in that of control group. The binding of SDF-1 to CXCR4 induces OA cartilage degeneration. The catabolic processes can be disrupted by pharmacologic blockade of SDF-1/CXCR4 signaling. Together, these findings raise the possibility that disruption of the SDF-1/CXCR4 signaling can be used as a therapeutic approach to attenuate cartilage degeneration