A revision of the African genus Mesanthemum (Eriocaulaceae)

Mesanthemum is a genus comprising 16 species in the family Eriocaulaceae and is native to Africa and Madagascar. Eriocaulaceae are characterised by a basal tuft or rosette of narrow leaves and small flowers in heads. Mesanthemum can be recognised by diplostemonous flowers and fused glandular pistillate petals. While most Mesanthemum species are large perennial herbs, two small ephemeral species from West Africa, M. albidum and M. auratum differ from the rest of the genus by their shorter life cycle, smaller size, simpler floral structures and different seed surface patterning. A molecular phylogenetic study, morphological comparisons and scanning electron microscope (SEM) examination of seed coat sculpture were carried out to determine whether they should be separated as a new genus. The molecular results indicate that the two ephemeral species are nested in the Mesanthemum clade. However, they are not closely related to each other. All species of Mesanthemum are here revised, including the description of a new species M. alenicola from Equatorial Guinea. An identification key is provided, together with taxonomic descriptions, synonymy and notes. Images of the seeds as seen under SEM are provided where available. Lectotypifications are provided for Mesanthemum albidum, M. bennae, M. pilosum, M. prescottianum, M. pubescens and M. variabile. A neotype is selected for M. rutenbergianum, which is synonymised with M. pubescens

Charge Scaling in Classical Force Fields for Lithium Ions in Polymers

Polymer electrolytes are of interest for applications in energy storage. Molecular simulations of ion transport in polymer electrolytes have been widely used to study the conductivity in these materials. Such simulations have generally relied on classical force fields. A peculiar feature of such force fields has been that in the particular case of lithium ions (Li+), their charge must be scaled down by approximately 20% to achieve agreement with experimental measurements of ion diffusivity. In this work, we present first-principles calculations that serve to justify the charge-scaling factor and van der Waals interaction parameters for Li+ diffusion in poly(ethylene glycol) (PEO) with bistriflimide (TFSI–) counterions. Our results indicate that a scaling factor of 0.79 provides good agreement with DFT calculations over a relatively wide range of Li+ concentrations and temperatures, consistent with past reports where that factor was adjusted by trial and error. We also show that such a scaling factor leads to diffusivities that are in quantitative agreement with experimental measurements

Numerical Study of Magnetic Island Coalescence Using Magnetohydrodynamics With Adaptively Embedded Particle-In-Cell Model

Collisionless magnetic reconnection typically requires kinetic treatments that are, in general, computationally expensive compared to fluid-based models. In this study, we use the magnetohydrodynamics with adaptively embedded particle-in-cell (MHD-AEPIC) model to study the interaction of two magnetic flux ropes. This innovative model embeds one or more adaptive PIC regions into a global MHD simulation domain such that the kinetic treatment is only applied in regions where kinetic physics is prominent. We compare the simulation results among three cases: 1) MHD with adaptively embedded PIC regions, 2) MHD with statically (or fixed) embedded PIC regions, and 3) a full PIC simulation. The comparison yields good agreement when analyzing their reconnection rates and magnetic island separations, as well as the ion pressure tensor elements and ion agyrotropy. In order to reach a good agreement among the three cases, large adaptive PIC regions are needed within the MHD domain, which indicates that the magnetic island coalescence problem is highly kinetic in nature where the coupling between the macro-scale MHD and micro-scale kinetic physics is important.Comment: 9 pages, 10 figure

Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation

Contemporary domain adaptation offers a practical solution for achieving cross-domain transfer of semantic segmentation between labeled source data and unlabeled target data. These solutions have gained significant popularity; however, they require the model to be retrained when the test environment changes. This can result in unbearable costs in certain applications due to the time-consuming training process and concerns regarding data privacy. One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data. Despite this, the referring style transfer module still faces issues with computation cost and over-fitting problems. To address this problem, we propose a novel framework called Informative Data Mining (IDM) that enables efficient one-shot domain adaptation for semantic segmentation. Specifically, IDM provides an uncertainty-based selection criterion to identify the most informative samples, which facilitates quick adaptation and reduces redundant training. We then perform a model adaptation method using these selected samples, which includes patch-wise mixing and prototype-based information maximization to update the model. This approach effectively enhances adaptation and mitigates the overfitting problem. In general, we provide empirical evidence of the effectiveness and efficiency of IDM. Our approach outperforms existing methods and achieves a new state-of-the-art one-shot performance of 56.7\%/55.4\% on the GTA5/SYNTHIA to Cityscapes adaptation tasks, respectively. The code will be released at \url{https://github.com/yxiwang/IDM}.Comment: Accepted by ICCV 202

Alzheimer’s disease and retinal neurodegeneration share a consistent stress response of the neurovascular unit

Background: The pathogenesis of Alzheimer’s disease (AD) is characterized by neuronal injury, activation of microglia and astrocytes, deposition of amyloid-beta and secondary vessel degeneration. In the polycystic kidney disease (PKD) rat model, we observed neuronal injury, microglial activation and vasoregression. We speculated that this neuroretinal degeneration shares important pathogenetic steps with AD. Therefore, we determined the activation of astrocytes and the accumulation of amyloid-beta in PKD retinae. Methods: Immunohistochemistry of PKD retinae for vimentin, carboxymethyllysin, beta-Amyloid 1-42, High-Mobility-Group-Protein B1 and amyloid protein precursor was performed. Results: Adjunct to astrocyte activation, accumulation of beta-Amyloid 1-42 and High-Mobility-Group-Protein B1 in astrocytes and around vessels of the superficial network was found in PKD retinae prior to the onset of vasoregression. Amyloid precursor protein was localized adjacent to the outer segment of photoreceptors in PKD and control rats. The parallel appearance of AD-related peptides indicates an alarmine based response to photoreceptor degeneration and secondary vasoregression. Conclusion: The model has broad overlap with AD and may be suitable to study beneficial pharmacological concepts. Copyright (c) 2012 S. Karger AG, Base

Volumetric Wireframe Parsing from Neural Attraction Fields

The primal sketch is a fundamental representation in Marr's vision theory, which allows for parsimonious image-level processing from 2D to 2.5D perception. This paper takes a further step by computing 3D primal sketch of wireframes from a set of images with known camera poses, in which we take the 2D wireframes in multi-view images as the basis to compute 3D wireframes in a volumetric rendering formulation. In our method, we first propose a NEural Attraction (NEAT) Fields that parameterizes the 3D line segments with coordinate Multi-Layer Perceptrons (MLPs), enabling us to learn the 3D line segments from 2D observation without incurring any explicit feature correspondences across views. We then present a novel Global Junction Perceiving (GJP) module to perceive meaningful 3D junctions from the NEAT Fields of 3D line segments by optimizing a randomly initialized high-dimensional latent array and a lightweight decoding MLP. Benefitting from our explicit modeling of 3D junctions, we finally compute the primal sketch of 3D wireframes by attracting the queried 3D line segments to the 3D junctions, significantly simplifying the computation paradigm of 3D wireframe parsing. In experiments, we evaluate our approach on the DTU and BlendedMVS datasets with promising performance obtained. As far as we know, our method is the first approach to achieve high-fidelity 3D wireframe parsing without requiring explicit matching.Comment: Technical report; Video can be found at https://youtu.be/qtBQYbOpVp

Analysis of Urban Impervious Surface in Coastal Cities: A Case Study in Lianyungang, China

Impervious surface is an important indicator of the level of urbanization. It is of great significance to study the impervious surface to promote the sustainable development of the city. In the process of urban development, the increase of impervious surface cities is bound to be accompanied by a reduction of one or more types of land use in the city. This paper, taking Lianyungang as an example, introduces the methods of extracting urban impervious surface based on VIS model, NDVI (normalized vegetation index), MNDWI (modified normalized water body index), and unsupervised classification, analyzes the changes of impervious surface in Lianyungang from 1987 to 2014, and on this basis, analyzes the trend and driving forces of land use types in Lianyungang city in depth. The results show that the impervious surface of Lianyungang increased by a total of 29.70% between 1987 and 2014. While the impervious surface continues to increase, the area of cultivated land and coastal areas (including salt works and tidal flats) has been greatly reduced, and the types of land use have undergone significant changes

Spatial-temporal distribution and eutrophication evaluation of nutrients and trace metals in summer surface seawater of Yantai Sishili Bay, China

Due to coastal development expansion, an increasing influx of pollutants enters the sea through riverine input and land runoff, threatening coastal ecosystems and posing a risk of eutrophication. In this study, trace metals (Fe, Mn, Cu, and Zn), and nutrients (constituents of N, P, and Si) were assessed in the summer surface seawater of Yantai Sishili Bay (YSB), Northern China focusing on the determination of concentration, spatial-temporal distribution and sources identification, while exploring their correlations. It also aimed to clarify the eutrophication status and evaluate the linear relationships between eutrophication, trace metals, and nutrients in YSB. Over three years (2021–2023), the total dissolved concentrations of Fe, Mn, Cu, and Zn ranged from 4.79–26.71, 0.19–6.41, 0.26–1.53, and 0.74–13.12 µg/L, respectively. Concurrently, nutrient concentrations including NO2-, NO3-, NH4+, PO43-, and DSi exhibited a range of 0.37–11.66, 2.04–178.30, 1.69–70.01, 0.02–16.68, and 0.02–0.71 µg/L respectively. These concentrations revealed a gradual decrease from nearshore to offshore and the temporal variation also showed significant patterns from year to year, indicating distinct regional variations. The primary contributors to the trace metals and nutrients in the study region were recognized as external contributions stemming from natural, anthropogenic, and atmospheric deposition through correlation and principal component analysis. More specifically, riverine input and coastal farming contributed large amounts of nutrients to coastal waters, threatening a potential risk of eutrophication. The eutrophication evaluation expressed below the mild eutrophication level and was far lower than the other global and Chinese bays. The linear correlation between eutrophication and trace metals revealed a weak positive correlation but a significant correlation with nutrients. Despite the absence of significant eutrophication in the bay, potential risks were identified due to identifiable sources of nutrient and trace metal inputs. The findings provided insights to guide efforts in preventing and mitigating coastal eutrophication, as well as nutrient and trace metal pollution, in coastal cities

Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes

Robust autonomous driving requires agents to accurately identify unexpected areas in urban scenes. To this end, some critical issues remain open: how to design advisable metric to measure anomalies, and how to properly generate training samples of anomaly data? Previous effort usually resorts to uncertainty estimation and sample synthesis from classification tasks, which ignore the context information and sometimes requires auxiliary datasets with fine-grained annotations. On the contrary, in this paper, we exploit the strong context-dependent nature of segmentation task and design an energy-guided self-supervised frameworks for anomaly segmentation, which optimizes an anomaly head by maximizing the likelihood of self-generated anomaly pixels. To this end, we design two estimators for anomaly likelihood estimation, one is a simple task-agnostic binary estimator and the other depicts anomaly likelihood as residual of task-oriented energy model. Based on proposed estimators, we further incorporate our framework with likelihood-guided mask refinement process to extract informative anomaly pixels for model training. We conduct extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks, demonstrating that without any auxiliary data or synthetic models, our method can still achieves competitive performance to other SOTA schemes

Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo

PurposeThis study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic.MethodsThe analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16–24 and the linguistic characteristics of the posts.ResultsThe study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of “Affect” showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as “Negative emotion” and “Sadness” also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress.ConclusionThe findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment