The characteristics of local government debt governance: evidence from qualitative and social network analysis of Chinese policy texts

This paper takes 66 local government debt governance policy texts from 2009 to 2019 as sample, and constructs an analysis framework of ‘target debt - management measures - mechanism guarantee’, derives up with identifying the characteristics of determining local government debt governance in China. The results show as follow: (i) It attaches more importance to the policy design of ‘borrowing’, ‘repayment’ and ‘management’ on local government debt instead of the ‘usage’ in China. (ii) The evidence supports the relatively average coding distribution of each policy, but the content is quite different. (iii) It manifests the new development value of ‘people-oriented’ and the concept of collaborative governance in the policy design. (iv) It has formed a ‘centreedge’ model for the governance of the local government debt. This paper provides a new perspective for the study of the problems of local government debt governance, and puts forward policy recommendations for improving the governance of the local government debt

High extinction risk in large foraminifera during past and future mass extinctions.

There is a strong relationship between metazoan body size and extinction risk. However, the size selectivity and underlying mechanisms in foraminifera, a common marine protozoa, remain controversial. Here, we found that foraminifera exhibit size-dependent extinction selectivity, favoring larger groups (>7.4 log10 cubic micrometer) over smaller ones. Foraminifera showed significant size selectivity in the Guadalupian-Lopingian, Permian-Triassic, and Cretaceous-Paleogene extinctions where the proportion of large genera exceeded 50%. Conversely, in extinctions where the proportion of large genera was <45%, foraminifera displayed no selectivity. As most of these extinctions coincided with oceanic anoxic events, we conducted simulations to assess the effects of ocean deoxygenation on foraminifera. Our results indicate that under suboxic conditions, oxygen fails to diffuse into the cell center of large foraminifera. Consequently, we propose a hypothesis to explain size distribution-related selectivity and Lilliput effect in animals relying on diffusion for oxygen during past and future ocean deoxygenation, i.e., oxygen diffusion distance in body

Meigs syndrome was misdiagnosed as a malignant ovarian tumor: a case report

BackgroundMeigs syndrome is characterized by the association of a benign ovarian tumor, typically an ovarian fibroma, with pleural effusion and ascites.Case summaryThis report presents a case of a 54-year-old woman who was misdiagnosed with malignant ovarian neoplasm due to the presence of significant abdominal distension and elevated CA125 levels. Initial imaging at multiple external facilities suggested a left-sided malignant ovarian tumor, leading to unnecessary delays in treatment. Upon admission to our institution in April 2024, imaging confirmed a large pelvic mass, and subsequent diagnostic procedures indicated a likely fibroma. Surgical intervention revealed a left ovarian thecoma, and post-operative pathology confirmed the diagnosis. Notably, CA125 levels decreased from 335.1 U/ml to 164.6U/ml following surgery, and the patient showed significant clinical improvement.ConclusionThis case underscores the importance of considering Meigs syndrome in patients presenting with ovarian masses, pleural effusions, and elevated CA125, to prevent misdiagnosis and ensure timely management

Histidine‐mediated synthesis of chiral cobalt oxide nanoparticles for enantiomeric discrimination and quantification

Chiral transition metal oxide nanoparticles (CTMOs) are attracting a lot of attention due to their fascinating properties. Nevertheless, elucidating the chirality induction mechanism often remains a major challenge. Herein, the synthesis of chiral cobalt oxide nanoparticles mediated by histidine (Co3O4@L-His and Co3O4@D-His for nanoparticles synthesized in the presence of L- and D-histidine, respectively) is investigated. Interestingly, these CTMOs exhibit remarkable and tunable chiroptical properties. Their analysis by x-ray photoelectron, Fourier transform infrared, and ultraviolet-visible absorption spectroscopy indicates that the ratio of Co2+/Co3+ and their interactions with the imidazole groups of histidine are behind their chiral properties. In addition, the use of chiral Co3O4 nanoparticles for the development of sensitive, rapid, and enantioselective circular dichroism-based sensors is demonstrated, allowing direct molecular detection and discrimination between cysteine or penicillamine enantiomers. The circular dichroism response of the chiral Co3O4 exhibits a limit of detection and discrimination of cysteine and penicillamine enantiomers as low as 10 µm. Theoretical calculations suggest that the ligand exchange and the coexistence of both species adsorbed on the oxide surface are responsible for the enantiomeric discrimination. This research will enrich the synthetic approaches to obtain CTMOs and enable the extension of the applications and the discovery of new chiroptical properties.National Natural Science Foundation of China | Ref. 22271257Agencia Estatal de Investigación | Ref. PID2019-108954RB-I00Xunta de Galicia | Ref. ED431C 2020/09Universidade de Vigo/CISU

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Controlling supramolecular chirality in multicomponent self-assembled systems

Chirality exists as a ubiquitous phenomenon in nature, from molecular level l-amino acids, d-sugar, secondary structures of proteins, DNA, RNA, and nanoscale helices to macroscopic conch and even galaxy. The aggregation of molecular building blocks with or without chiral centers might bring about asymmetric spatial stacking, which further results in the appearance of nonsymmetry in extended scales like helical nanofibers. This phenomenon, known as supramolecular chirality, is an important branch of supramolecular and self-assembly chemistry, which relates intimately with biomimetics, asymmetric catalysis, and designing chiroptic advanced materials. One of the important research focuses among supramolecular chirality is about rational manipulation of chirality amplification and handedness, presenting a profound influence on the performance of resulting soft materials such as circularly polarized luminescence and cell adhesion on hydrogels. The control over supramolecular chirality normally relies on two factors, i.e., thermodynamic and kinetic variables dependent on molecular structural parameters and environmental contributions, respectively. Supramolecular chirality in two or more component-based systems places an emphasis on thermodynamic control as it occurs from either integrated coassembly or separated self-sorting, which is more sophisticated than that of single component systems. Thus, the study on supramolecular chirality in multicomponent systems could mimic complicated biosystems, allowing for better understanding about the origin of natural chirality and extended applications as biomimetics. To date, the exploration of supramolecular chirality in multicomponent systems is restricted on both fundamental and application aspects when compared to more matured single component systems. Over the past few years, we have carried out systematic studies on several systems expressing supramolecular chirality from chiral amplification or symmetry breaking. We emphasized more the thermodynamic control by introducing a second component to form noncovalent bonding like hydrogen bonding or coordination interactions. In this Account, we would specifically discuss rational manipulation of the occurrence, transfer, and inversion of supramolecular chirality by taking several of the latest representative examples. In the multicomponent systems, in addition to the building blocks with chiral centers, the second or third components could be structural analogues and achiral small molecules such as bipyridines, melamine, metal ions, inorganic nanomaterials, and even solvents. These second or third components are able to incorporate during the aggregation to form coassembly via noncovalent bonds, influencing spatial arrangements of building blocks within various dimensions from vesicles and nanofibers to organic/inorganic hybrids. Other than chirality, morphology, stimulus responsiveness, and properties could also be well tailored by controlling interactions between different components