Factors and structural paths of the changes in carbon emissions in China's provincial construction industries.

The changes in the carbon emissions in China's provincial construction industries are of high complexity. It is essential to understand the changes in the construction carbon emissions (CCEs) in China on the provincial scale. This study evaluates the factors and structural paths of the changes in provincial CCEs in China between 2012 and 2017 using the structural path decomposition analysis.

Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding Item Response Tree Models.

Two research streams on responses to Likert-type items have been developing in parallel: (a) unfolding models and (b) individual response styles (RSs). To accurately understand Likert-type item responding, it is vital to parse unfolding responses from RSs. Therefore, we propose the Unfolding Item Response Tree (UIRTree) model.

Pathological Changes and Cause of Death Associated with the Global Novel Coronavirus Disease (COVID-19).

The coronavirus disease 2019 (COVID-19) has been a global epidemic for more than three years, causing more than 6.9 million deaths. COVID-19 has the clinical characteristics of strong infectivity and long incubation period, and can cause multi-system damage, mainly lung damage, clinical symptoms of acute respiratory distress syndrome (ARDS) and systemic multiple organ damage.

Establishment of colloidal gold immunochromatography strip for rapid detection of Karen mikimotoi and its application.

In China, harmful algal blooms (HABs) are one of the most prominent ecological disasters in the coastal areas. Among the harmful algae species that cause HABs, Karen mikimotoi is a kind of algae that appear frequently. It can secrete hemolytic toxins and fish toxins such as glycolipids and unsaturated fatty, posing a significant threat to marine life.

Governing public health emergencies during the coronavirus disease outbreak: Lessons from four Chinese cities in the first wave.

The ongoing coronavirus disease (COVID-19) pandemic has had a far-reaching impact on urban living, prompting emergency preparedness and response from public health governance at multiple levels. The Chinese government has adopted a series of policy measures to control infectious disease, for which cities are the key spatial units. This research traces and reports analyses of those policy measures and their evolution in four Chinese cities: Zhengzhou, Hangzhou, Shanghai and Chengdu.

Proteomic analysis discovers potential biomarkers of early traumatic axonal injury in the brainstem.

Objective: Application of Tandem Mass Tags (TMT)-based LC-MS/MS analysis to screen for differentially expressed proteins (DEPs) in traumatic axonal injury (TAI) of the brainstem and to predict potential biomarkers and key molecular mechanisms of brainstem TAI.

Methods: A modified impact acceleration injury model was used to establish a brainstem TAI model in Sprague-Dawley rats, and the model was evaluated in terms of both functional changes (vital sign measurements) andstructural changes (HE staining, silver-plating staining and β-APP immunohistochemical staining). TMT combined with LC-MS/MS was used to analyse the DEPs in brainstem tissues from TAI and Sham groups.

The Role of Non-coding RNAs in Methamphetamine-Induced Neurotoxicity.

Methamphetamine (METH) is an amphetamine-type stimulant that is highly toxic to the central nervous system (CNS). Repeated intake of METH can lead to addiction, which has become a globalized problem, resulting in multiple public health and safety problems. Recently, the non-coding RNA (ncRNA) has been certified to play an essential role in METH addiction through various mechanisms.

Multi-omics analysis reveals GABAergic dysfunction after traumatic brainstem injury in rats.

Background: Traumatic brainstem injury (TBSI) is one of the forms of brain injury and has a very high mortality rate. Understanding the molecular mechanism of injury can provide additional information for clinical treatment.

Materials And Methods: In this study, we detected transcriptome, proteomics, and metabolome expression changes in the brainstem of TBSI rats, and comprehensively analyzed the underlying mechanisms of TBSI.

Development of a test strip for rapid detection of Gymnodinium catenatum.

Harmful algal blooms (HABs) are major ecological and environmental problems in China's coastal waters and seriously threaten the stability of the marine ecosystem and human health. Gymnodinium catenatum is a toxic red tide dinoflagellate. It can produce paralytic shellfish toxins (PSP), which cause serious hazards to marine organisms, public health, and safety.

Genomic Prediction Strategies for Dry-Down-Related Traits in Maize.

For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding.

Characterization of precipitation from citrus vinegar during ageing: chemical constituents, formation mechanism and anti-proliferative effect.

Precipitation formation commonly occurs in the ageing step of fermented citrus vinegar. Hitherto, the chemical characteristics and biological properties of precipitates remain unveiled. This study focused on investigating the chemical profile, formation mechanism and biological repurposing of precipitates.

Sustainability performance of global chemical industry based on green total factor productivity.

The sustainability of the chemical industry is crucial for achieving global sustainable production. The sustainability performance of global chemical industry is influenced by many issues synergistically and has not been fully quantified. Systematic analysis from multiple perspectives, such as resource savings, economic growth, and environmental improvement, is urgently needed to support effective macro-policy decisions.

KDM1A Identified as a Potential Oncogenic Driver and Prognostic Biomarker via Multi-Omics Analysis.

Background: Lysine-specific demethylase 1A (KDM1A) is a histone demethylation enzyme and a crucial epigenetic factor for multiple pathological pathways that mediate carcinogenesis and immunogenicity. Although increasing evidence supposes the association between KDM1A and cancers, no systematic multi-omics analysis of KDM1A is available.

Methods: We systematically evaluated the KDM1A expression of various cancer and normal tissues and the unique relationship between KDM1A expression and prognosis of cancer cases based on The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) database.

A new method of energy-related carbon dioxide emissions estimation at the provincial-level: A case study of Shandong Province, China.

The accuracy of carbon dioxide (CO) emissions at the provincial-level is urgently needed for China to peak CO emission and implement a carbon reduction plan. However, the current estimation methods have some drawbacks, such as not meeting China's situation, data obsolescence, and relatively high uncertainty. Moreover, there are large differences in estimated results among previous studies.

Estimates of carbon dioxide emissions based on incomplete condition information: a case study of liquefied natural gas in China.

Recent calculations of carbon dioxide (CO) emissions have faced challenges because data consist of only partial information, which is called "incomplete information." According to the emission factor method, energy consumption and CO emission factors with incomplete information may lead to unmatched multiplication between themselves, which affects accuracy and increases uncertainties in emission results. To address a specific case of incomplete information that has not been fully explored, we studied the effects of incomplete condition information on the estimates of CO emissions from liquefied natural gas (LNG) in China.

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation.

We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs.